Temporomandibular Joint Analysis

TMJ Assessment and Dysfunction Evaluation

TMJ examination
TMD assessment
jaw joint evaluation

1934 (Costen syndrome); 1960s+ (modern understanding)

Multiple innovators (Costen, Laskin, Okeson, et al.)

Temporomandibular Joint (TMJ) analysis is a systematic clinical assessment and imaging evaluation of the jaw joint, including the articular disc, condyle, and associated musculature. TMJ analysis evaluates joint function, detects dysfunction (TMD), and guides diagnosis and treatment planning for jaw pain, clicking, locking, and limited opening. Comprehensive assessment integrates clinical examination with imaging (magnetic resonance imaging, cone-beam computed tomography) to characterize joint status and tailor treatment.

Okeson, J. P. (2020). Management of temporomandibular disorders and occlusion (8th ed.). Elsevier.

Schiffman, E., Ohrbach, R., Truelove, E., et al. (2014). Diagnostic criteria for temporomandibular disorders (DC/TMD) for clinical and research applications. Journal of Oral & Facial Pain and Headache, 28(1), 6-27.

de Kanter, R. J., Truin, G. J., Burgersdijk, R. C., et al. (1993). Prevalence in the Dutch adult population and a meta-analysis of the association with potential risk factors. Journal of Dental Research, 72(11), 1509-1518.


Ten-Meter Walk Test

The Ten-Meter Walk Test (10MWT) is a straightforward performance assessment measuring gait speed over a 10-meter distance. Used extensively in neurological rehabilitation, the 10MWT provides objective data on walking velocity, a key indicator of functional mobility, recovery after stroke, and response to intervention in individuals with gait impairments from various causes.

Gait speeds in clinical populations. Journal of the American Geriatrics Society, 40(10), 1071-1076.

Collen, F. M., Wade, D. T., & Robb, G. F. (1991). The Rivermead Mobility Index: A further development of the Rivermead Motor Assessment. International Disability Studies, 13(2), 50-54.


Tephrochronology

Tephrochronology is a chronometric and stratigraphic technique that uses volcanic ash layers (tephra) as time markers to date and correlate archaeological and geological deposits. Pioneered by Icelandic geologist Sigurdur Thorarinsson in 1944, it exploits the fact that large explosive volcanic eruptions deposit distinctive ash layers across vast geographic regions instantaneously in geological time. Each tephra layer serves as a chronological marker horizon that can be identified, characterized, and dated, enabling archaeologists to synchronize deposits across multiple sites.

Thorarinsson, S. (1944). Tefrokronologiska studier på Island. Geografiska Annaler, 26(1-2), 1-217.

Lowe, D. J., & Hunt, J. B. (1992). Tephrochronology and archaeology: an introduction. In C. M. Turney, K. A. Dodson, & K. C. Ker (Eds.), Quaternary of New Zealand (pp. 27-35). Royal Society of New Zealand Bulletin.

Froese, D. G., Westgate, J. A., Reyes, A. V., Enkin, R. J., & Preece, S. J. (2006). Ancient bacteria and a dinosaur-like smell. Geology, 34(9), 757-760.


TERCOM

Terrain Contour Matching (TERCOM) is a terrain-aided navigation method that corrects position estimates by matching altimeter measurements against a stored digital elevation map (DEM). Developed by Boeing in the 1980s for cruise missile guidance, TERCOM enables accurate navigation in GPS-denied environments by exploiting the unique terrain signature at each location. TERCOM remains essential for missile guidance, autonomous underwater vehicles, and systems operating in jamming scenarios.

Golden, J. P. (1983). Terrain contour matching (TERCOM): A cruise missile guidance aid. In In-Flight Measurement Technology. AGARD Conference Proceedings No. 336, 3–1 to 3–16.

Grejner-Brzezinska, D. A. (2001). Terrain-aided navigation using neural networks. In Proceedings of the 14th International Technical Meeting of the Satellite Division of the US Institute of Navigation, Salt Lake City, Utah, 11–14 September 2001, 2033–2041.

Hein, G. W., Brynjarsson, F., Denks, H., Godet, J., Landau, H., & Erker, S. (2012). Enhancements to the GNSS augmentation service WAAS and modernization. In Proceedings of the Institute of Navigation, GNSS 2012 Conference.


Terzaghi Consolidation

Terzaghi One-Dimensional Consolidation Theory

Primary consolidation
Soil settlement
Effective stress

Terzaghi consolidation theory describes how water-saturated clay soils compress over time as excess pore water pressure dissipates and effective stress increases. Formulated by Karl Terzaghi in 1943, this foundational theory enables prediction of settlement rates for foundations on compressible soils, a critical design concern in geotechnical engineering.

Diffusion equation for pore pressure dissipation and soil settlement

Terzaghi, K. (1943). Theoretical Soil Mechanics. John Wiley & Sons.

Taylor, D. W. (1948). Fundamentals of Soil Mechanics. John Wiley & Sons.

Kelly, R. B. (1995). Settlement of embankments on soft soils. Journal of Geotechnical Engineering, 121(5), 373-384.


Test Anxiety Inventory

The Test Anxiety Inventory measures the situational anxiety experienced during examinations, distinguishing between cognitive worry and physiological emotionality. Developed by Spielberger in 1980, the TAI provides educators and clinicians with a validated assessment of test-specific anxiety—a prevalent barrier to academic success that affects performance disproportionately to ability. Early identification and targeted intervention can substantially improve both well-being and exam performance.

Spielberger, C. D. (1980). Test Anxiety Inventory: Preliminary professional manual. Consulting Psychologists Press.

Zeidner, M. (1998). Test anxiety: The state of the art. Kluwer Academic.

academic-burnout-scale academic-resilience-scale study-skills-assessment academic-help-seeking-scale procrastination-assessment-scale


Westside Test Anxiety Scale

The Westside Test Anxiety Scale (WTAS) is a 10-item self-report questionnaire measuring the intensity of anxiety and worry experienced before, during, and after academic tests. Developed by Ralph Driscoll and validated in 2007, the WTAS assesses the cognitive (worry, negative self-talk) and somatic (tension, trembling, nausea) dimensions of test anxiety. It is widely used in educational psychology, academic counseling, and cognitive-behavioral research to identify students at risk for test anxiety and to monitor intervention effectiveness.

Driscoll, R. (2007). Westside Test Anxiety Scale validation. Paper presented at the Association for the Advancement of Educational Research, International Convention, Chicago.


Test Equating

Test Eşitleme (Test Equating)
score equating
equipercentile equating
IRT true-score equating
linear equating

Kolen & Brennan (foundational treatise, 2004/2014); Holland & Dorans (2006)

Test equating is a family of statistical methods that converts scores earned on one test form onto the score scale of another form, so that scores from different administrations or versions can be compared and reported on a common metric. The foundational modern treatment is Kolen and Brennan (2004/2014); Holland and Dorans (2006) provide the authoritative chapter-length overview within the field of educational measurement.

Score transformation / latent-scale calibration

Concordance table mapping raw scores on one test form to the score scale of another

Raw or IRT-scaled scores from two or more test forms

NEAT (Non-Equivalent groups with Anchor Test) or single-group / random-groups

Kolen, M.J. & Brennan, R.L. (2014). Test Equating, Scaling, and Linking: Methods and Practices (3rd ed.). Springer.

Holland, P.W. & Dorans, N.J. (2006). Linking and Equating. In R.L. Brennan (Ed.), Educational Measurement (4th ed., pp. 187–220). American Council on Education / Praeger.

item-response-theory generalizability-theory confirmatory-factor-analysis classical-test-theory rasch-model


Test-Retest Reliability

stability reliability
temporal stability
repeatability coefficient
TRT reliability

Test-retest reliability quantifies the temporal consistency of a measure by correlating scores obtained from the same participants on two separate occasions. It is a cornerstone of psychometric validation, directly indicating whether a scale or instrument yields stable scores when the underlying construct has not changed.

Continuous or ordinal scores from two administrations

Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.

Anastasi, A. & Urbina, S. (1997). Psychological Testing (7th ed.). Prentice Hall.

cronbachs-alpha mcdonalds-omega interrater-reliability confirmatory-factor-analysis measurement-invariance generalizability-theory


TRIG

TRIG
Faschingbauer TRIG
Revised Inventory of Grief

Thomas R. Faschingbauer, Sidney Zisook, Richard DeVaul

The Texas Revised Inventory of Grief (TRIG) is a 21-item multidimensional measure developed by Faschingbauer, Zisook, and DeVaul in 1987 to assess both past grief behaviors (how the person grieved when the death occurred) and present grief feelings (current emotional response to loss). The TRIG is unique in distinguishing historical grief response from contemporary grief state, providing a comprehensive temporal and dimensional profile of bereavement.

Faschingbauer, T. R., Zisook, S., & DeVaul, R. (1987). The Texas Revised Inventory of Grief. In S. Zisook (Ed.), Biopsychosocial aspects of bereavement (pp. 111–124). American Psychiatric Press.

inventory-complicated-grief hogan-grief-reaction-checklist grief-experience-questionnaire anticipatory-grief-scale


Text Classification

Text Classification (Text Categorization)

text categorization
document classification
topic classification
metin sınıflandırma

Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.

Assign predefined category labels to text

Output: Category label per document

Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.

Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer.

sentiment-analysis document-clustering keyword-extraction tf-idf


Text Coherence Scoring

Text Coherence Scoring (Local Coherence Modeling)

coherence modeling
local coherence assessment
Metin Tutarlılık Puanlaması

Text coherence scoring computes a document-level coherence score with machine learning, rooted in the entity-based local coherence model introduced by Barzilay and Lapata (2008). It measures how well the sentences of a text hang together, using either an entity-grid model, a graph-based approach, or a transformer-based model.

Entity-grid model / graph-based / transformer-based

Output: Document-level coherence score

Barzilay, R. & Lapata, M. (2008). Modeling Local Coherence: An Entity-Based Approach. Computational Linguistics, 34(1), 1-34.

Guinaudeau, C. & Strube, M. (2013). Graph-based Local Coherence Modeling. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), 93-103.

automatic-text-evaluation sentiment-analysis text-classification bert-embeddings


Text Complexity Analysis

Text Complexity and Readability Analysis

readability analysis
linguistic complexity assessment
Metin Karmaşıklığı Analizi

Text complexity analysis measures the linguistic difficulty of a text along dimensions such as syntactic complexity (sentence length, embedded clauses), lexical density, and referential chains. Grounded in readability research consolidated by Vajjala and Meurers (2014) and Crossley and colleagues (2011), it turns prose into quantitative scores that estimate how hard a document is to read.

Syntactic complexity, lexical density, referential cohesion

Flesch, Gunning-Fog, SMOG and related readability indices

Output: Readability and complexity scores per text

Vajjala, S. & Meurers, D. (2014). Readability Assessment for Text Simplification: From Analysing Documents to Identifying Sentential Simplifications. International Journal of Applied Linguistics, 165(2), 194-222.

Crossley, S.A., Allen, D.B. & McNamara, D.S. (2011). Text Readability and Intuitive Simplification: A Comparison of Readability Formulas. Reading in a Foreign Language, 23(1), 84-101.


Text Deduplication

Text Deduplication (Near-Duplicate Detection)

Metin Tekilleştirme (Near-Duplicate Detection)

Andrei Z. Broder (MinHash / Resemblance theory, 1997)

Text deduplication is a corpus-quality pipeline that identifies and removes exact and near-duplicate documents from large text collections. Grounded in Andrei Broder's 1997 resemblance theory, it is widely used to improve dataset quality for machine learning model training, search engine indexing, and any downstream NLP task that assumes a non-redundant corpus.

Text preprocessing / corpus quality pipeline

MinHash + Locality-Sensitive Hashing (LSH)

50 documents (LSH recommended for large collections)

Broder, A.Z. (1997). On the Resemblance and Containment of Documents. Compression and Complexity of SEQUENCES.

Lee, K. et al. (2022). Deduplicating Training Data Makes Language Models Better. ACL 2022.

sentiment-analysis tf-idf text-classification bert-embeddings topic-modeling


Text Infilling

cloze procedure
cloze test
masked language modeling
span infilling
Metin Doldurma (Text Infilling / Cloze)

1953 (cloze); 2019 (neural span infilling)

Wilson L. Taylor (cloze procedure, 1953); modern span infilling by Zhu et al. (2019)

Text infilling is a natural-language-processing task that completes missing words, phrases, or spans in a document by exploiting the surrounding context. Introduced as the cloze procedure by Wilson L. Taylor in 1953 as a readability measure, it was reformulated for neural models by Zhu et al. (2019) and is now used for data augmentation, writing assistance, and language-model evaluation.

Input: Text with one or more masked tokens, spans, or blanks

Output: Predicted text filling each masked position

Taylor, W.L. (1953). Cloze Procedure: A New Tool for Measuring Readability. Journalism Quarterly, 30(4), 415-433.

Zhu, C., Zeng, M., & Huang, X. (2019). Text Infilling. arXiv:1901.00158.

sentiment-analysis bert-embeddings text-classification language-modeling text-generation named-entity-recognition


Text Network Analysis

Metin Ağ Analizi (Text Network Analysis)

2011 (Paranyushkin); 2005 (Diesner & Carley)

Dmitry Paranyushkin; Jana Diesner & Kathleen M. Carley

Text network analysis models the words or concepts in a text as nodes and their co-occurrences as edges, then uses network metrics to reveal the structure of meaning. The approach was advanced by Diesner and Carley (2005) for communication networks and by Paranyushkin (2011) for tracing the pathways of meaning circulation in text.

Output: Network graph with centrality and structural metrics

Paranyushkin, D. (2011). Identifying the Pathways for Meaning Circulation Using Text Network Analysis. Nodus Labs.

Diesner, J. & Carley, K. M. (2005). Exploration of Communication Networks from the Enron Email Corpus. SIAM International Conference on Data Mining, Workshop on Link Analysis, Counterterrorism and Security.

collocation-analysis frequency-analysis-text dependency-parsing sentiment-analysis


Text Normalization

Text Normalization (Noisy-Text Standardisation)

Metin Normalleştirme
noisy-text normalization
text standardisation
lexical normalisation

Text normalization is an NLP preprocessing pipeline that converts noisy, abbreviated, or misspelled text — such as SMS messages, social-media posts, and OCR output — into a clean, standardised form. It is a prerequisite step for virtually every downstream NLP task, ensuring that inconsistent surface forms do not degrade tokenisation, parsing, or classification. The method gained systematic academic treatment through Baldwin and Li (2015) and Sproat and Jaitly (2017).

noisy raw text (SMS, social media, OCR output)

Noise-type identification; target-language lexicon or language model available

Baldwin & Li (2015); Sproat & Jaitly (2017)

Baldwin, T. & Li, Y. (2015). An In-depth Analysis of the Effect of Text Normalization in Twitter. NAACL-HLT 2015.

Sproat, R. & Jaitly, N. (2017). RNN Approaches to Text Normalization: A Challenge. arXiv:1611.00068.

sentiment-analysis tokenization named-entity-recognition pos-tagging spell-checking


Text Regression

text-as-data regression
predicting numeric outcomes from text
Metin Tabanlı Regresyon

Text-based regression predicts a continuous target variable using features extracted from text — TF-IDF scores, embeddings, or n-grams — as the independent variables. Building on the text-as-data programme consolidated by Gentzkow, Kelly and Taddy (2019), it lets a numeric outcome such as a price, a rating, or a sentiment score be estimated directly from documents, and is widely used in social-science, economics, and finance applications.

Text features (TF-IDF, embeddings, n-grams)

Gentzkow, M., Kelly, B. & Taddy, M. (2019). Text as Data. Journal of Economic Literature, 57(3), 535-574.

Taddy, M. (2013). Measuring Political Sentiment on Twitter: Factor Optimal Design for Multinomial Inverse Regression. Technometrics, 55(4), 415-425.

tf-idf text-classification sentiment-analysis bert-embeddings


Text Segmentation

topic segmentation
discourse segmentation
linear text segmentation
Metin Bölümleme (Text Segmentation)

Text segmentation divides a long document into meaningful sections (segments) along topic or discourse boundaries. Introduced for subtopic passages by Marti A. Hearst's TextTiling (1997), it supports document-structure analysis and the detection of topic transitions in continuous text.

NLP document-structure / topic-boundary detection

Output: Ordered segments bounded by detected topic shifts

Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64.

Choi, F.Y.Y. (2000). Advances in Domain Independent Linear Text Segmentation. NAACL.

sentiment-analysis ngram-language-model language-identification tf-idf


Text Summarization

automatic summarization
extractive summarization
abstractive summarization
Otomatik Metin Özetleme

Automatic text summarization is a natural-language-processing task that condenses long documents into shorter summaries while preserving their key information. It works through one of two families of approaches — extractive summarization, which selects the most important spans from the source, or abstractive summarization, which generates new text. The field was consolidated by Nenkova and McKeown (2011), and sequence-to-sequence models such as BART (Lewis et al., 2020) advanced the abstractive side.

NLP text-generation / text-reduction task

Output: Shorter summary preserving key information

Nenkova, A. & McKeown, K. (2011). Automatic Summarization. Foundations and Trends in Information Retrieval.

Lewis, M. et al. (2020). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. ACL.

keyword-extraction semantic-similarity document-clustering sentiment-analysis


Textual Criticism

Textual Criticism (Lower Criticism / Editorial Criticism)

lower criticism
editorial criticism
philological criticism
manuscript criticism

Antiquity; modern systematic method c. 1850s (Lachmann)

Classical philologists (Karl Lachmann foremost in systematic method)

Textual criticism is a systematic philological method for identifying, comparing, and evaluating variant readings across multiple manuscript or print witnesses of a text in order to reconstruct the most accurate version of the original — or the author's intended — text. Applied since antiquity to classical, biblical, and literary works, it remains the foundational editorial method in classical studies, biblical scholarship, medieval studies, and critical editing of literary works.

Humanistic / philological research method

Manuscripts, printed editions, inscriptions, archival textual documents

West, M. L. (1973). Textual Criticism and Editorial Technique Applicable to Greek and Latin Texts. Teubner.

hermeneutic-analysis historical-archival-research oral-history-method discourse-analysis content-analysis comparative-textual-criticism


Textual Entailment

Textual Entailment (Natural Language Inference, NLI)

Metinsel Çıkarsama (Textual Entailment / NLI)

Textual entailment, also known as natural language inference (NLI), is the natural-language-processing task of deciding whether one piece of text (the premise) entails a second piece of text (the hypothesis), contradicts it, or is neutral with respect to it. Formalised by the PASCAL Recognising Textual Entailment Challenge (Dagan, Glickman & Magnini, 2006) and broadened by the MultiNLI corpus (Williams, Nangia & Bowman, 2018), it underpins question answering and fact-verification pipelines.

Input: Premise-hypothesis text pairs

Dagan, I., Glickman, O. & Magnini, B. (2006). The PASCAL Recognising Textual Entailment Challenge.

Williams, A., Nangia, N. & Bowman, S. (2018). A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. NAACL.

zero-shot-classification text-classification sentiment-analysis word-sense-disambiguation


Texture Profile Analysis

Texture Profile Analysis (TPA) is an objective, mechanical method that simulates mastication (chewing) to measure the textural properties of food products. Developed by Bourne in 1968, TPA uses a texture analyzer (a machine that applies defined forces and movements to a sample) to generate a force-time curve from which multiple texture attributes (hardness, springiness, chewiness, cohesiveness, adhesiveness) are extracted and quantified.

Bourne, M. C. (1968). Texture profile of foods. Journal of Food Science, 33(3), 280-283.

Meilgaard, M. C., Carr, B. T., & Civille, G. V. (2006). Sensory evaluation techniques (4th ed.). CRC Press.


TF-IDF

Term Frequency–Inverse Document Frequency Vectorization

term weighting
tf-idf weighting
TF-IDF Vektörizasyonu

TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.

Text vectorization / term-weighting scheme

Input: Document collection (corpus) of text

Output: Weighted document-term vectors

~100 documents for meaningful IDF weights

Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.


TGARCH model

Threshold Generalized Autoregressive Conditional Heteroscedasticity Model

Threshold GARCH
TGARCH
GJR-GARCH
asymmetric GARCH

Zakoian (1994); Glosten, Jagannathan & Runkle (1993)

The Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage effect. TGARCH captures this asymmetry through a threshold indicator that switches on when the previous period's shock was negative.

Financial or macroeconomic time series with conditional heteroscedasticity

Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.

Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801.

arch-model egarch-model dcc-garch-model garch-model vector-autoregression arima-model


Theil-Sen Estimator

Theil-Sen Estimator (Median Slope Regression)

Theil-Sen Tahmincisi
Theil-Sen regression
median slope estimator
Sen's slope estimator
Kendall-Theil robust line

The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%.

Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389.

Theil, H. (1950). A Rank-Invariant Method of Linear and Polynomial Regression Analysis. Proceedings of the Royal Netherlands Academy of Sciences, 53, 386-392, 521-525, 1397-1412.

ols-regression quantile-regression least-trimmed-squares winsorized-estimation bootstrap-inference permutation-test


Thematic Analysis

Thematic Analysis (TA) is a qualitative research methodology for identifying, analyzing, and reporting patterns (themes) in qualitative data. Developed systematically by Virginia Braun and Victoria Clarke (2006), TA is flexible and accessible, applicable across diverse theoretical frameworks and data types, making it one of the most widely used qualitative methods in psychology, health research, and social sciences.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.

Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code development. Sage Publications.


Thematic Evolution Analysis

Thematic Evolution Analysis in Science Mapping

TEA
thematic development analysis
temporal thematic mapping
longitudinal theme analysis

Manuel J. Cobo and colleagues (University of Granada)

Thematic evolution analysis is a bibliometric technique that divides a body of literature into consecutive time periods and tracks how research themes emerge, consolidate, split, merge, or disappear across those periods. By combining co-word analysis, clustering, and strategic diagrams for each time slice, it produces a dynamic picture of a field's intellectual development rather than a static snapshot.

Bibliographic records (keywords, abstracts, titles) from academic databases (WoS, Scopus)

Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402.

Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146–166.

bibliometric-analysis co-word-analysis science-mapping scientometric-analysis systematic-literature-review co-citation-analysis


Theodorsen Flutter

flutter analysis
aeroelastic stability
Theodorsen's function

Theodorsen flutter analysis is a classical aeroelastic method for predicting the onset of flutter, a self-excited oscillation where aerodynamic forces interact with elastic structural motion to cause rapid growth of oscillations. Developed by Theodore Theodorsen in 1935, the method uses frequency-domain analysis with Theodorsen's function to compute aerodynamic forces on oscillating wings. Flutter speed prediction is essential for aircraft certification and structural design.

Theodorsen, T. (1935). General theory of aerodynamic instability and the mechanism of flutter. NACA Report No. 496.

Bisplinghoff, R. L., Ashley, H., & Halfman, R. L. (1955). Aeroelasticity. Addison-Wesley.

Wright, J. R., & Cooper, J. E. (2007). Introduction to Aircraft Aeroelasticity and Loads (2nd ed.). John Wiley & Sons.


Theoretical Domains Framework

Theoretical Domains Framework (TDF): A 14-Domain Behavioral Change Model for Understanding Implementation Barriers and Designing Interventions

TDF
theoretical domains
behaviour change framework

Michie, S., Johnston, M., Abraham, C., et al.

The Theoretical Domains Framework (TDF) is a 14-domain model that integrates constructs from 33 behavior change and implementation theories to identify barriers and facilitators to professional and public behavior change. Developed by Michie et al. (2005) to provide a practical tool for implementation scientists and behavior change specialists, the TDF helps systematically assess 'why' healthcare professionals or patients do (or do not) adopt evidence-based practices, and guides the design of tailored behavior change interventions.

Michie, S., Johnston, M., Abraham, C., Lawton, R., Parker, D., & Walker, A. (2005). Making psychological theory useful for implementing evidence based practice: A consensus approach. Quality and Safety in Health Care, 14(1), 26-33.

Atkins, L., Francis, J., Islam, R., O'Connor, D., Patey, A., Ivers, N., ... & Michie, S. (2017). A guide to using the Theoretical Domains Framework (TDF) to develop interventions to change professional practice and public health behaviour: PLOS ONE guide. PLOS Medicine, 14(4), e1002335.

Lawton, R., Heyhoe, J., Louch, G., Ingleson, E., Eyles, E., & Clements, J. (2016). Using the Theoretical Domains Framework (TDF) to understand healthcare professionals' behaviour: A systematic review. Implementation Science, 11, 123.

behavior-change-wheel cfir-framework knowledge-translation implementation-outcome-taxonomy normalization-process-theory


Theory of Constraints

TOC
Constraint Management
Bottleneck Theory
Kısıtlar Teorisi

The Theory of Constraints (TOC) is a management philosophy and continuous improvement framework introduced by Eliyahu Goldratt in his 1984 novel The Goal and formalized in his 1990 book. TOC holds that every system has at least one constraint — a bottleneck that limits the system's overall throughput — and that systematically identifying and addressing that constraint is the most effective lever for improving performance. It is widely applied in manufacturing, project management, supply chains, and service operations.

Goldratt, E. M. (1990). Theory of Constraints. North River Press.


Theory of Planned Behavior Questionnaire

The Theory of Planned Behavior (TPB) is a psychological framework developed by Icek Ajzen in 1991 to predict and understand deliberate human behavior. The TPB questionnaire measures four core constructs that explain why people intend to perform (or not perform) a specific behavior: attitudes toward the behavior, subjective norms, perceived behavioral control, and behavioral intention. This measure is widely used in health behavior research, particularly for understanding health promotion, disease prevention, and lifestyle change initiatives.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.


Therapeutic Alliance Scale

THAS
TAS
Therapeutic Working Alliance Scale

The Therapeutic Alliance Scale (THAS) is a clinician-rated measure of the quality of the therapeutic relationship and working alliance, developed by Raue, Goldfried, and Barkham. Distinct from client-rated measures like the Working Alliance Inventory, the THAS captures the therapist's perception of goal alignment, task agreement, and emotional bond. It is used primarily in research to examine alliance from the therapist perspective and to understand therapist–client congruence in alliance perception.

Raue, P. J., Goldfried, M. R., & Barkham, M. (1997). The therapeutic alliance in psychodynamic-interpersonal and cognitive-behavioral therapy. Journal of Consulting and Clinical Psychology, 65(4), 582–587.

Hartley, D. E., & Strupp, H. H. (1983). The therapeutic alliance: Its relationship to outcome in brief psychotherapy. In J. Masling (Ed.), Empirical studies of psychoanalytic theories (Vol. 1, pp. 1–37). Lawrence Erlbaum.

working-alliance-inventory session-rating-scale common-factors-questionnaire collaborative-study-psychotherapy


Therapeutic Drug Monitoring

Drug Level Monitoring
Serum Drug Level Monitoring
Clinical Pharmacokinetic Monitoring
İlaç Düzeyi İzlemi

Therapeutic Drug Monitoring (TDM) is a clinical pharmacokinetic practice in which drug concentrations are measured in a patient's blood to guide individualized dosing. It applies principally to drugs with narrow therapeutic windows—where the margin between efficacy and toxicity is small—such as aminoglycosides, vancomycin, cyclosporine, and antiepileptics. Developed as a formal discipline in the 1980s, TDM integrates measured concentrations with pharmacokinetic modeling to calculate patient-specific dose regimens.

Clinical measurement and dose-optimization framework

Defined by minimum effective and minimum toxic concentrations

Spector, R., Park, G. D., Johnson, G. F., & Vesell, E. S. (1988). Therapeutic drug monitoring. Clinical Pharmacology & Therapeutics, 43(4), 345–353.


Therapeutic Touch Assessment Scale

TTAS
TT Assessment
Therapeutic Touch Competency Scale

The TTAS measures the application and outcomes of therapeutic touch (TT), an energy-based healing modality developed by Krieger and Kunz in which practitioners use intentional hand movements proximal to or in contact with the patient's body to promote relaxation, reduce pain, and facilitate healing. Used both as a competency assessment for practitioners and as an outcome measure for patients receiving TT.

Practitioner skill assessment and patient outcome measure

Krieger, D. (1979). Therapeutic touch: How to use your hands to help or heal. New York: Prentice-Hall.

Barret, E. A. M., & Yates, M. E. (2002). The Rogerian science of unitary human beings in nursing practice. In M. E. Parker (Ed.), Nursing theories and nursing practice (2nd ed., pp. 47–75). Philadelphia: F.A. Davis.

Hutchison, T. L. (2008). Therapeutic touch: A review of the evidence. Journal of Nursing Administration, 38(10), 439–445.

holistic-caring-inventory spiritual-care-competence-scale music-therapy-assessment-tool attitudes-cam-scale


The Therapy Process Observational Coding System (TPOCS) is a comprehensive observer-rated method for classifying and quantifying therapist and client utterances in psychotherapy sessions. Using Stiles's taxonomy of verbal response modes (e.g., Advisement, Reflection, Interpretation, Disclosure), the TPOCS enables detailed analysis of what therapists and clients are doing moment-by-moment: who is talking, what mode (technique), and how frequently. It is used in process research to understand mechanisms of change, train therapists, and examine whether therapy modalities differ in their in-session behavior.

Stiles, W. B. (1992). Describing talk: A taxonomy of verbal response modes. Newbury Park, CA: Sage Publications.

Hill, C. E., & Lambert, M. J. (2004). Methodological issues in studying psychotherapy processes and outcomes. In M. J. Lambert (Ed.), Bergin and Garfield's handbook of psychotherapy and behavior change (5th ed., pp. 84–135). John Wiley & Sons.

collaborative-study-psychotherapy working-alliance-inventory helpful-aspects-of-therapy session-rating-scale


Thermal Comfort Assessment

Thermal Comfort Assessment and Prediction

thermal comfort evaluation
adaptive comfort model
PMV-PPD analysis

Comfort and indoor environmental quality

Thermal Comfort Assessment is a method for evaluating indoor environmental conditions to predict whether occupants will feel thermally comfortable. Pioneered by Povl Ole Fanger in the 1970s, it combines measurements of air temperature, humidity, air speed, and thermal properties of clothing and activity to determine comfort zones and identify remedial actions.

Fanger, P. O. (1972). Thermal Comfort: Analysis and Applications in Environmental Engineering. Danish Technical Press, Copenhagen.

Dearlove, J., Kharade, M. K., Datta, S. (2012). Survey of Comfort and Thermal Preferences in Mixed-Mode Buildings. Proceedings of the 10th International Conference on Healthy Buildings.

Nicol, J. F., Humphreys, M. A. (2002). Adaptive Thermal Comfort and Sustainable Thermal Standards for Buildings. Energy and Buildings, 34(6), 563-572.


Thermal Resistance Network

The Thermal Resistance Network method uses electrical circuit analogy to solve heat transfer problems. It treats heat flow as analogous to electric current, thermal resistance analogous to electrical resistance, and temperature difference analogous to voltage potential. This powerful conceptual framework enables engineers to analyze complex multi-layer heat transfer systems systematically.

Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.

Holman, J. P. (2009). Heat Transfer (10th ed.). McGraw-Hill.


Thermal Wind

Thermal wind
Vertical wind shear
Barotropic

Jacobbian insights from geostrophic flow

The thermal wind relationship is a fundamental meteorological principle that links vertical wind shear to horizontal temperature gradients. It states that wind speed increases with height in the direction of warming—a direct consequence of hydrostatic and geostrophic balance combined with the ideal gas law.

Holton, J. R. (2004). An Introduction to Dynamic Meteorology (4th ed.). Academic Press.

Bluestein, H. B. (1993). Synoptic-dynamic meteorology in midlatitudes. Volume 2: Observations and Theory of Weather Systems. Oxford University Press.


Thermogravimetric Analysis

Thermogravimetric Analysis (TGA) is a thermal characterization technique that continuously measures mass loss or gain of a material as a function of temperature (or time at constant temperature). Developed systematically by William Wendlandt and colleagues in the 1960s, TGA identifies thermal transitions (evaporation, decomposition, oxidation, reduction) and quantifies composition of polymers, pharmaceuticals, ceramics, and other materials. The derivative signal (DTG) highlights transition temperatures. When combined with gas analysis (MS, FTIR), decomposition products are identified.

Wendlandt, W. W. (1986). Thermal Analysis (3rd ed.). John Wiley & Sons.

Haines, P. J. (Ed.). (2012). Principles of Thermal Analysis and Calorimetry (2nd ed.). Royal Society of Chemistry.

Vyazovkin, S., et al. (2020). ICTAC Kinetics Committee recommendations for performing kinetic computations on thermal analysis data. Thermochimica Acta, 689, 178597.


Thermoluminescence Dating

TL dating
thermoluminescence chronometry

Thermoluminescence (TL) dating is a chronometric technique that determines the age of pottery, ceramics, and sediments by measuring light emitted when heated to high temperatures. Pioneered by Michael Aitken in the 1960s, it quantifies the accumulated radiation dose stored in mineral crystal lattices. The method revolutionized archaeological dating by enabling scientists to date ceramic vessels and fired clay objects directly, providing absolute chronologies for human occupation sites worldwide.

Aitken, M. J. (1985). Thermoluminescence Dating. Academic Press.

Prescott, J. R., & Hutton, J. T. (1994). Cosmic ray contributions to dose rates for luminescence and ESR dating: Large depths and long-term time variations. Radiation Measurements, 23(2-3), 497-500.

Wintle, A. G. (2005). Luminescence dating: laboratory procedures and protocols. Radiation Measurements, 27(5-6), 769-817.


Thesis and Dissertation

Thesis and Dissertation (Graduate Research Degree Requirements)

master's thesis
PhD dissertation
doctoral thesis
graduate thesis
ETD

Medieval university tradition (12th century onward)

A thesis (Master's level) or dissertation (doctoral level) is an original research document required for completion of graduate degree programs, serving as the capstone of a student's graduate training. A Master's thesis typically represents 1–2 years of research; a PhD dissertation, 3–5 years. Both require original empirical or theoretical contribution, demonstration of mastery in a discipline, and successful oral defense before faculty committee. Theses and dissertations establish academic credentials, document scholarly work, and are archived in institutional and national repositories (ProQuest, EThOS) for access by scholars worldwide.

American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). APA.

ProQuest Dissertations & Theses Global. https://www.proquest.com/pqdtglobal

EThOS: British Library Electronic Theses Online Service. https://ethos.bl.uk

original-research-article literature-review-article academic-writing-process research-methodology


Theta Method

Theta Method for Time Series Forecasting

Theta Yöntemi — M3 Tahmin Yarışması Birincisi

The Theta Method is a univariate time-series forecasting model introduced by Assimakopoulos and Nikolopoulos in 2000. It decomposes a series into two theta lines that capture its long-run trend and its short-run dynamics, forecasts each line separately, and combines them by a weighted average. Its simplicity and accuracy made it the winner of the M3 forecasting competition.

Univariate time-series forecasting model

Decomposition into theta lines, combined by weighted average

Winner of the M3 forecasting competition

Assimakopoulos, V. & Nikolopoulos, K. (2000). The Theta Model: A Decomposition Approach to Forecasting. International Journal of Forecasting, 16(4), 521-530.

Makridakis, S. & Hibon, M. (2000). The M3-Competition: Results, Conclusions and Implications. International Journal of Forecasting, 16(4), 451-476.

ols-regression arima exponential-smoothing holt-winters ets-model


Thin-Layer Chromatography

Thin-Layer Chromatography (TLC) is a planar chromatographic technique that separates compounds based on their differential affinities for a mobile and stationary phase. Developed by Egon Stahl in 1956, TLC remains one of the most accessible and widely used analytical methods in organic and inorganic chemistry, laboratories, and quality control.

Sherma, J. (2003). Planar Chromatography. Analytical Chemistry, 75(12), 2783–2811.

Fried, B., & Sherma, J. (2004). Thin-Layer Chromatography: Techniques and Applications (4th ed.). Marcel Dekker.


Think-Aloud Protocol

Think-Aloud Protocol for Usability Testing

Talk-Aloud Protocol
Concurrent Thinking Aloud
TA

K. Anders Ericsson and Herbert A. Simon, adapted to HCI by Clayton Lewis

The Think-Aloud Protocol is a usability testing method in which participants verbalize their thoughts while completing tasks on a system. As users navigate an interface, they continuously narrate their observations, interpretations, and reasoning, allowing researchers to understand their mental models, decision-making, and frustration points. Originating from cognitive psychology research by Ericsson and Simon (1980), this method was adapted for HCI by Clayton Lewis and has become one of the most widely used techniques for identifying usability problems and understanding user behavior.

Protocol for capturing user cognition and decision-making during task execution

Ericsson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87(3), 215–251.

Lewis, C. (1982). Using the 'thinking aloud' method in cognitive interface design. Technical Report RC 9265, IBM Research Center.

retrospective-think-aloud contextual-inquiry cognitive-walkthrough pluralistic-walkthrough


TFEQ

Three-Factor Eating Questionnaire Revised (TFEQ-R21)

Stunkard and Messick Three-Factor Eating

The TFEQ is a self-report instrument measuring three distinct psychological dimensions of eating behaviour: cognitive restraint (conscious dieting efforts), disinhibition (loss of control over eating when triggered by stress or environmental cues), and hunger (subjective appetite and satiety responsiveness). Developed by Stunkard and Messick in 1985, the original 51-item instrument has been refined into a 21-item version (TFEQ-R21) widely used in obesity research, eating behaviour studies, and nutritional psychology.

Stunkard, A. J., & Messick, S. (1985). The Three-Factor Eating Questionnaire to measure dietary restraint, disinhibition, and hunger. Journal of Psychosomatic Research, 29(1), 71–83.

Karlsson, J., Persson, L. O., Sjöström, L., & Sullivan, M. (2000). Psychometric properties and factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study. International Journal of Obesity, 24(12), 1715–1725.

Cappelleri, J. C., Bushmakin, A. G., Gerber, R. A., Leidy, N. K., Sexton, C. C., Lowe, M. R., & Karlsson, J. (2009). Psychometric analysis of the Three-Factor Eating Questionnaire-R21: Results from a large diverse sample of obese and non-obese participants. International Journal of Obesity, 33(6), 611–620.

ede-q scoff-questionnaire body-shape-questionnaire binge-eating-scale yale-food-addiction-scale


3PL IRT

Three-Parameter Logistic Item Response Theory Model

3PL IRT — Üç Parametreli Madde Tepki Modeli

The three-parameter logistic (3PL) model, introduced by Allan Birnbaum in 1968, is an item response theory model that describes the probability of a correct response to a binary test item as a function of three item-level parameters — difficulty, discrimination, and a lower asymptote representing guessing — and one person-level parameter representing latent ability.

Item response model / latent trait model

Item difficulty (b), discrimination (a), and guessing (c) parameters; person ability (θ)

Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In F. M. Lord & M. R. Novick (Eds.), Statistical theories of mental test scores (pp. 397–479). Addison-Wesley.

Baker, F. B. & Kim, S. H. (2004). Item response theory: Parameter estimation techniques (2nd ed.). Marcel Dekker.

two-pl-irt rasch-model exploratory-factor-analysis cfa cronbach-alpha


Three-Stage Least Squares

3SLS
system instrumental variables
Üç Aşamalı En Küçük Kareler (3SLS)

Three-Stage Least Squares is a system estimator for simultaneous-equation models that accounts for the correlation of error terms across equations. Introduced by Zellner and Theil in 1962, it combines two-stage least squares with the seemingly-unrelated-regression idea to estimate all equations jointly and more efficiently.

Generalised least squares applied to a system of two-stage least squares equations

Zellner, A. & Theil, H. (1962). Three-Stage Least Squares: Simultaneous Estimation of Simultaneous Equations. Econometrica, 30(1), 54–78.

two-stage-least-squares ols-regression seemingly-unrelated-regression system-gmm instrumental-variables


Three-Way Decisions

3WD
Trisecting-and-Acting
Tri-partition Decision Making
Üç Yönlü Kararlar

Three-Way Decisions (3WD) is a decision-theoretic framework, introduced by Yiyu Yao in 2010, that partitions the universe of objects into three regions—positive (accept), negative (reject), and boundary (abstain)—using probabilistic rough set theory. Unlike binary classifiers that force every object into one of two classes, 3WD explicitly acknowledges uncertainty by allowing a third option: deferring judgment when available evidence is insufficient for a confident decision.

Output: Accept / Reject / Abstain (three regions)

Yao, Y. (2010). Three-way decisions with probabilistic rough sets. Information Sciences, 180(3), 341–353.


Threshold Panel VAR

The Threshold Panel VAR extends the standard vector autoregression framework to accommodate regime-switching behavior where relationships change when a threshold variable crosses a critical level. Introduced by Hansen (1996) and applied to panels by Caner and Hansen (2001), it allows different dynamic relationships across regimes (e.g., expansions versus recessions) while exploiting the cross-sectional dimension of panel data. This nonlinear framework captures state-dependent policy effects and economic mechanisms.

Hansen, B. E. (1996). Inference when a nuisance parameter is not identified under the null hypothesis. Econometric Theory, 12(3), 386-414.

Caner, M., & Hansen, B. E. (2001). Threshold autoregression with a unit root. Econometric Theory, 17(4), 1-36.


Threshold Regression

Eşik Değer Regresyonu (Threshold Regression)

Threshold regression is a nonlinear, regime-switching model in which the regression parameters take different values above and below an estimated threshold value of a threshold variable. The sample-splitting and threshold-estimation framework was developed by Bruce E. Hansen (2000) and is widely used for time-series and panel data with structural breaks and regime-dependent relationships.

Conditional least squares with grid search over the threshold

Bootstrap confidence interval for the threshold (Hansen, 2000)

Hansen, B. E. (2000). Sample Splitting and Threshold Estimation. Econometrica, 68(3), 575-603.

ols-regression panel-fixed-effects quantile-regression ardl-bounds-test nardl-model


Thurstone Scaling

Thurstone Scaling (Law of Comparative Judgment)

Thurstone's Method of Equal-Appearing Intervals

Thurstone Scaling, formally the Law of Comparative Judgment, is a psychometric model introduced by Louis Leon Thurstone in 1927 for deriving interval-level scale values from pairwise comparison data. By assuming that each stimulus evokes a normally distributed discriminal process on a psychological continuum, the method converts proportions of preference judgments into z-scores and recovers the latent positions of stimuli, enabling rigorous attitude and preference measurement.

Psychological measurement and attitude scaling model

Input: Pairwise comparison frequencies

Output: Interval-level scale values on a psychological continuum

Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34(4), 273–286.


TED-QoL

Thyroid Eye Disease Quality of Life Questionnaire

GO-QoL
Graves Ophthalmopathy Quality of Life

Graves' ophthalmopathy-specific quality of life

Caroline Terwee, Markus Gerding, Frank Dekker

The TED-QoL (also known as GO-QoL, Graves' Ophthalmopathy Quality of Life questionnaire) is a 16-item disease-specific instrument assessing quality of life impacts in patients with thyroid eye disease (TED), the ophthalmologic manifestation of Graves' disease. Developed by Terwee, Gerding, and colleagues in 2001, it captures both functional vision limitations and psychological distress related to the characteristic eye changes (exophthalmos, lid retraction, diplopia, appearance concerns). It is the gold-standard outcome measure for TED quality of life assessment.

Terwee, C. B., Gerding, M. N., Dekker, F. W., et al. (2001). Development of a disease-specific questionnaire for patients with Graves' ophthalmopathy: The GO-QoL. Br J Ophthalmol, 82(7), 773-779.

Gerding, M. N., Terwee, C. B., Dekker, F. W., et al. (2003). Quality of life in patients with Graves' ophthalmopathy is markedly impaired: Measurement by the Medical Outcomes Study instrument. Thyroid, 7(11), 885-889.


ThyPRO

Thyroid-Related Patient Reported Outcomes Scale

Torquil Watt, Jens Bjørner, Marianne Groenvold

ThyPRO is a comprehensive patient-reported outcome measure assessing the quality of life impact of thyroid disease and its treatment across 13 dimensions. Developed by Watt and colleagues in 2009, it is the most extensively validated thyroid-specific instrument, covering both physical and psychological domains relevant to patients with hyperthyroidism, hypothyroidism, and thyroid cancer.

Watt, T., Bjorner, J. B., Groenvold, M., et al. (2009). Establishing construct validity for the thyroid-related patient reported outcomes (ThyPRO): An initial examination. J Clin Endocrinol Metab, 94(9), 3572-3580.

Watt, T., Groenvold, M., Bjorner, J. B., et al. (2012). Validity and responsiveness of the Danish version of ThyPRO and comparison with the Perceived Deficits Questionnaire. Value Health, 15(3), 528-534.


Tidal Harmonic Analysis

Tidal Constituents
Harmonic Tidal Prediction

Tidal harmonic analysis is a mathematical method that decomposes observed sea level or current time series into a sum of sinusoidal components with specific frequencies, amplitudes, and phases corresponding to astronomical tidal constituents. Developed by William Thomson (Lord Kelvin) in 1867, harmonic analysis enables prediction of tides and understanding of tidal dynamics in coastal regions.

Godin, G. (1972). The Analysis of Tides. University of Toronto Press.

Pugh, D. T., & Woodworth, P. L. (2014). Sea-Level Science: Understanding Tides, Surges, Tsunamis and Mean Sea-Level Changes. Cambridge University Press.


TiDE

Time-series Dense Encoder
TiDE model
Dense Encoder for Long-term Forecasting
Yoğun Kodlayıcı Zaman Serisi Modeli

TiDE (Time-series Dense Encoder) is an MLP-based encoder-decoder architecture for long-term multivariate time-series forecasting, introduced by Abhimanyu Das and colleagues at Google Research in 2023. The model encodes past time-series observations together with static and dynamic covariates through stacked dense (MLP) layers, then decodes a latent representation into future forecasts. TiDE demonstrates that simple linear and dense architectures can match or outperform Transformer-based models on standard long-term forecasting benchmarks while being significantly faster.

MLP-based encoder-decoder for long-term time-series forecasting

Dense MLP encoder-decoder with covariate projection

Input: Lookback window with optional static and dynamic covariates

Das, A., Kong, W., Leach, A., Mathur, S., Sen, R., & Yu, R. (2023). Long-term forecasting with TiDE: Time-series dense encoder. Transactions on Machine Learning Research.


TIFN-CODAS

TIFN-CODAS (Triangular Intuitionistic Fuzzy Number CODAS (Daami Remadi & Frikha 2023)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Combinative Distance-based Assessment under Triangular Intuitionistic Fuzzy uncertainty (TIFN: {(a1,a2,a3); (a'1,a2,a'3)}; a'1 ≤ a1 ≤ a2 ≤ a3 ≤ a'3) — MCGDM with linguistic-to-TIFN translation

Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems

ahp anp bwm critic entropy if-entropy merec swara


Tight-Binding Model

The Tight-Binding (TB) model is a simplified semi-empirical approach for computing electronic band structures and properties of solids. Formulated by Slater and Koster in 1954, TB treats electron hopping between atomic sites as the dominant interaction, enabling efficient calculations of band dispersion for a wide variety of materials.

Slater, J. C., Koster, G. F. (1954). Simplified LCAO method for the periodic potential problem. Physical Review, 94, 1498–1524.

Ashcroft, N. W., Mermin, N. D. (1976). Solid State Physics. Holt, Rinehart and Winston.

Mahan, G. D. (2000). Many-Particle Physics (3rd ed.). Kluwer Academic/Plenum Publishers.


Tillage Erosion Model

Soil Movement and Erosion Assessment from Tillage Operations

Tillage soil loss
Soil redistribution model
Erosion prediction

M. J. Lindstrom, W. W. Nelson, T. E. Schumacher

Tillage Erosion Model is a physical transport and modeling pipeline for predicting soil movement and redistribution caused by tillage operations on sloping land. Developed by soil scientists (Lindstrom, Nelson, Lobb) in the 1990s–2000s, this method quantifies how plowing, disking, and other soil-disturbing implements physically move soil downslope, leading to long-term productivity loss on upper slopes and soil accumulation in valleys.

Physical transport and modeling pipeline

Lindstrom, M. J., Nelson, W. W., & Schumacher, T. E. (1992). Soil movement by tillage as affected by slope. Soil Science Society of America Journal, 56(4), 1104-1108.

Lobb, D. A., Kachanoski, R. G., & Miller, M. H. (2003). Tillage translocation and tillage erosion as processes of soil redistribution on agricultural hillslopes. In Soil Erosion Research for the 21st Century: Proceedings of the International Symposium (pp. 15-17).

soil-fertility-management crop-growth-simulation nitrogen-use-efficiency irrigation-scheduling-etref precision-agriculture-ndvi


Timber Harvest Scheduling

harvest scheduling
timber rotation
forest planning

Timber harvest scheduling is an optimization method that determines which forest stands should be harvested and when, to achieve management objectives (economic return, sustained yield, biodiversity, wildlife habitat) while respecting constraints (minimum harvest age, ending inventory level, adjacent-stand restrictions). It integrates growth models, economic data, and spatial forest inventory to generate long-term management plans spanning decades. Harvest scheduling is essential for operational forest management and landscape-level planning.

Johnson, K. N., & Scheurman, H. L. (1977). Techniques for prescribing optimal timber harvest and investment under different objectives. Forest Science Monograph 18.

Bettinger, P., Boston, K., Siry, J. P., & Grebner, D. L. (2009). Forest Management and Planning. Academic Press, 2nd edition.


Timbre Analysis

Timbre Analysis and Characterization Algorithm

tone color analysis
spectral characterization
timbre descriptor extraction

Timbre analysis is the computational characterization and modeling of tone color—the perceived quality that distinguishes one instrument from another even at the same pitch and loudness. Pioneered by Grey (1977), timbre analysis extracts acoustic descriptors that characterize spectral shape, temporal dynamics, and harmonic content. It underlies instrument identification, music similarity assessment, and audio retrieval. Unlike melody and rhythm, timbre is high-dimensional and context-dependent, making it one of the most challenging aspects of music analysis.

Acoustic feature extraction and analysis

Grey, J. M. (1977). Multidimensional perceptual scaling of musical timbres. The Journal of the Acoustical Society of America, 61(5), 1270-1277.

Peeters, G., Giordano, B. L., Susini, P., Misdariis, N., & McAdams, S. (2011). The Timbre Toolbox: Extracting audio descriptors from musical signals. Journal of the Acoustical Society of America, 130(5), 2902-2916.

Seetharaman, P., Wlodarczyk, B., & Wichern, G. (2017). A categorical query-by-timbre system for musical audio. In Proceedings of the International Society for Music Information Retrieval Conference.

instrument-recognition music-genre-classification music-similarity-measure pitch-detection-algorithm vocal-separation


Time-Dependent Cox Regression

Cox Regression with Time-Varying Covariates

time-varying covariate Cox model
extended Cox model
Zamana Bağlı Kovaryatlı Cox Regresyonu

Cox, D. R. (extended formulation by Therneau & Grambsch)

Time-dependent Cox regression is an extension of the standard Cox proportional hazards model, introduced through the counting-process formulation developed by Therneau and Grambsch (2000), that allows one or more predictor variables to take different values at different points in a subject's follow-up period. It is the method of choice whenever a covariate — such as a laboratory measurement, a medication dose, or a disease severity score — changes over time rather than remaining fixed from study entry.

Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.

cox-ph kaplan-meier joint-model-survival frailty-model fine-gray-model flexible-parametric-survival


Time-Dependent DFT

Time-Dependent Density Functional Theory (TDDFT) extends DFT to excited states and time-dependent phenomena. Formulated by Runge and Gross in 1984, TDDFT enables calculation of excitation energies, optical spectra, and charge-transfer processes with moderate computational cost, making it invaluable for photochemistry and materials science.

Runge, E., Gross, E. K. (1984). Density-functional theory for time-dependent systems. Physical Review Letters, 52, 997–1000.

Casida, M. E. (1995). Time-dependent density-functional response theory for molecules. In Recent Advances in Density Functional Methods. World Scientific.

Huix-Rotllant, M., et al. (2020). Assessment of time-dependent density functional theory for excited states. In Handbook of Excited State Spectroscopy. World Scientific.


Time-MoE

Time-MoE (Mixture-of-Experts Time-Series Foundation Model)

Time Mixture-of-Experts
Time-MoE Foundation Model
Sparse Time-Series Transformer
Zaman Karışık Uzmanlar Modeli

Time-MoE is a billion-scale autoregressive foundation model for universal time-series forecasting, introduced by Shi et al. in 2024 and accepted at ICLR 2025. It combines a decoder-only transformer architecture with sparse Mixture-of-Experts (MoE) feed-forward layers, enabling the model to scale to billions of parameters while activating only a small subset of expert networks per token—dramatically increasing capacity without proportional compute cost.

Sparse mixture-of-experts autoregressive foundation model

Shi, X., Wang, S., Nie, Y., Li, D., Ye, Z., Wen, Q., & Jin, M. (2024). Time-MoE: Billion-scale time series foundation models with mixture of experts. ICLR 2025.


Time-Motion GPS

Time-Motion Analysis and GPS Movement Tracking

GPS analysis
movement tracking
workload quantification
physical demands

Time-motion analysis with GPS and micro-sensor technology quantifies the movement patterns, workload, and physical demands during training or match play in team sports. Pioneered by Osgnach and colleagues (2010), modern GPS units track athletes' positions in real-time, calculating distance covered, velocity profiles, and acceleration/deceleration frequencies. Combined with heart rate and other sensor data, GPS analysis provides comprehensive workload quantification enabling coaching staff to monitor player fatigue, balance training intensity, and prevent injury. GPS is now standard in elite soccer, rugby, Australian Rules football, and other intermittent sports.

Gregory, P., & Drust, B. (2007). Physical demands of rugby union: quantification of accelerations and movements patterns in play. Journal of Strength and Conditioning Research, 21(2), 309-314.

Osgnach, C., Poser, S., Bernardini, R., Rinaldo, R., & di Prampero, P. E. (2010). Energy cost and metabolic power in elite soccer: a new match analysis approach. Medicine and Science in Sports and Exercise, 42(1), 170-178.

Cummins, C., Orr, R., O'Connor, H., & West, C. (2013). Global positioning systems (GPS) and microtechnology sensors in team sports: A systematic review. Sports Medicine, 43(10), 1025-1042.

acute-chronic-workload-ratio banister-trimp session-rpe heart-rate-recovery


Time-of-Flight PID

ToF
flight time measurement
velocity measurement

Time-of-Flight (ToF) particle identification measures the time taken for a particle to travel a known distance, enabling determination of the particle's velocity and mass. This complementary technique to Cherenkov and ionization energy loss provides robust particle separation across wide momentum ranges in modern detectors.

Heilbronn, L. H., & Zeitlin, C. (2010). Measurement of particle identification efficiencies. Nuclear Instruments and Methods in Physics Research Section B, 268(23-24), 3577–3583.

Adinolfi, M., et al. (2013). Performance of the LHCb VELO detector and vertex reconstruction. Journal of Instrumentation, 8(12), P12008.

Stelzer, B., et al. (2015). Time-of-flight measurements in particle detection. Reviews of Scientific Instruments, 86(1), 013301.


Time series approximate Bayesian computation

Time Series Approximate Bayesian Computation

likelihood-free inference for time series

Beaumont, Zhang & Balding (2002) for ABC; Toni et al. (2009) for dynamical/time-series extension

Time series ABC is a likelihood-free Bayesian inference method that estimates the posterior distribution of model parameters for dynamical or time-indexed systems by comparing summary statistics of simulated trajectories to those of the observed series, bypassing the need to evaluate an analytic likelihood. It is particularly valuable for complex mechanistic or stochastic models whose likelihoods are intractable.

Toni, T., Welch, D., Strelkowa, N., Ipsen, A. & Stumpf, M. P. H. (2009). Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface, 6(31), 187–202.

Sisson, S. A., Fan, Y. & Beaumont, M. A. (Eds.) (2018). Handbook of Approximate Bayesian Computation. CRC Press.

approximate-bayesian-computation sequential-monte-carlo particle-filter dynamic-bayesian-inference kalman-filter time-series-bayesian-inference


Time series Bayesian hierarchical model

TSBHM
Bayesian hierarchical time series
hierarchical dynamic Bayesian model
multilevel Bayesian time series

West & Harrison (dynamic models); Gelman et al. (hierarchical Bayesian framework)

A time series Bayesian hierarchical model combines the hierarchical (multilevel) Bayesian framework with a dynamic state-space structure to analyse temporal data collected on multiple units or groups. Priors encode beliefs about both within-unit dynamics and cross-unit variation, and the posterior is obtained via MCMC or sequential Monte Carlo, yielding full probabilistic forecasts with calibrated uncertainty.

Bayesian hierarchical model for time series

West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.

hierarchical-bayesian-inference dynamic-bayesian-network kalman-filter bayesian-regression time-series-mcmc multilevel-bayesian-inference


Time series Bayesian inference

Bayesian Inference for Time Series Models

Bayesian time series analysis
Bayesian state-space modeling
probabilistic time series inference
BSTS

Time series Bayesian inference applies Bayes' theorem sequentially to time-ordered observations, maintaining a full probability distribution over hidden states and model parameters at every time step. This framework unifies state-space models, dynamic linear models, and particle filters, producing calibrated uncertainty for both filtering (real-time) and retrospective smoothing tasks.

Prado, R. & West, M. (2010). Time Series: Modeling, Computation, and Inference. CRC Press.

kalman-filter particle-filter dynamic-bayesian-network bayesian-regression sequential-monte-carlo hierarchical-bayesian-inference


Time series Bayesian model averaging

Bayesian model averaging for time series

Hoeting, Madigan, Raftery, Volinsky (BMA); Raftery et al. for dynamic/time-series extensions

Time series Bayesian model averaging (TS-BMA) combines forecasts from an ensemble of time series models — such as AR, VAR, or state-space specifications — by weighting each model by its posterior probability given observed data. Rather than selecting one model and discarding uncertainty about which model is best, TS-BMA integrates over model uncertainty, producing forecasts that are more robust and better calibrated than any single model alone.

time series (univariate or multivariate, continuous)

Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401.

Raftery, A. E., Kárný, M., & Ettler, P. (2010). Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technometrics, 52(1), 52–66.

bayesian-model-averaging dynamic-model-averaging bayesian-regression time-series-bayesian-inference kalman-filter sequential-monte-carlo


Time-series ChIP-seq peak calling

Time-series Chromatin Immunoprecipitation Sequencing Peak Calling

longitudinal ChIP-seq analysis
dynamic ChIP-seq peak calling
time-course ChIP-seq
temporal chromatin profiling

2008–2012 (ChIP-seq); time-series extensions ~2015–2020

ENCODE Consortium; extended by Haiminen et al. and broader epigenomics community

Time-series ChIP-seq peak calling extends standard chromatin immunoprecipitation sequencing analysis to samples collected at multiple time points. By identifying and comparing protein-DNA binding peaks across a temporal dimension, the method reveals how transcription factor occupancy, histone modifications, or chromatin remodeler binding evolve during biological processes such as differentiation, circadian cycles, or stimulus response.

ChIP-seq read alignments (BAM/BED) at multiple time points; matched input controls

Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., ... & Snyder, M. (2012). ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research, 22(9), 1813–1831.

Haiminen, N., Karlebach, G., Kharchenko, P. V., & Lähdesmäki, H. (2018). TimeChIP: time-series peak calling for ChIP-seq data. Bioinformatics, 34(24), 4161–4167.


Time-series copy number variation analysis

Time-Series Copy Number Variation Analysis

longitudinal CNV analysis
temporal copy number analysis
time-series CNV profiling
serial CNV analysis

Developed from foundational CNV methods (Olshen et al. 2004; Ding et al. 2010) extended to longitudinal tumor genomics frameworks

Time-series copy number variation (CNV) analysis is a computational genomics pipeline that characterizes chromosomal gains and losses across multiple sequential samples from the same individual or tumor. By comparing copy number profiles at successive time points — such as diagnosis, mid-treatment, relapse — it reconstructs the clonal dynamics and evolutionary trajectories driving genome instability, enabling researchers to track how sub-populations expand, contract, or acquire new aberrations over time.

Paired-end whole-genome or whole-exome sequencing data from multiple time points per sample

Dentro, S. C., et al. (2021). Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell, 184(8), 2239-2254.

Zaccaria, S., & Raphael, B. J. (2020). Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data. Nature Communications, 11(1), 4301.


Time-series Epigenome-wide Association Study

Longitudinal Epigenome-wide Association Study

time-series EWAS
longitudinal EWAS
repeated-measures EWAS
dynamic methylation association study

Extended from EWAS (Rakyan et al., 2011); longitudinal designs formalised by multiple groups ~2010s

A time-series epigenome-wide association study (time-series EWAS) extends the classic cross-sectional EWAS design to longitudinal settings, measuring DNA methylation across the entire epigenome at multiple time points within the same subjects. The goal is to identify CpG sites whose methylation levels change systematically over time, or to characterise how epigenetic associations with an exposure or phenotype evolve across developmental stages, treatment periods, or disease trajectories.

Longitudinal epigenomic association pipeline

Genome-wide DNA methylation arrays or bisulfite sequencing at multiple time points per subject

Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., ... & Clark, S. J. (2016). Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology, 17(1), 208.

Waterland, R. A., Kellermayer, R., Laritsky, E., Rayco-Solon, P., Harris, R. A., Travisano, M., ... & Prentice, A. M. (2010). Season of conception in rural Gambia affects DNA methylation at putative human metastable epialleles. PLoS Genetics, 6(12), e1001252.


Time-series eQTL analysis

Time-series Expression Quantitative Trait Loci Analysis

dynamic eQTL analysis
longitudinal eQTL mapping
ts-eQTL
temporal eQTL

2010s–2019 (concept established earlier; dynamic framework formalized ~2019)

Multiple groups; formalized by Strober et al. and others in the context of cellular differentiation (2019)

Time-series eQTL analysis identifies genetic variants (eQTLs) whose effect on gene expression changes over time or across developmental stages. By combining longitudinal RNA-seq data with individual genotypes, the method captures how the same SNP can activate, silence, or reshape gene regulation at different time points — revealing the temporal architecture of the genome's regulatory program in processes such as differentiation, disease progression, and environmental response.

Time-stamped RNA-seq gene expression data paired with genotype (SNP array or WGS)

Fair, B. J., et al. (2020). Gene expression variability in human and chimpanzee populations share common determinants. eLife, 9, e59929.

Strober, B. J., et al. (2019). Dynamic genetic regulation of gene expression during cellular differentiation. Science, 364(6447), 1287–1290.

eqtl-mapping differential-expression-analysis genome-wide-association-study co-expression-network-analysis chromatin-accessibility-analysis mediation-analysis


Time-Series Gene Set Enrichment Analysis

longitudinal GSEA
dynamic GSEA
time-course GSEA
TS-GSEA

2005 (GSEA foundation); time-series adaptations 2007–2014

Extension of GSEA (Subramanian et al., 2005); time-series adaptations developed through maSigPro (Conesa lab) and related tools

Time-series gene set enrichment analysis (TS-GSEA) extends the classical GSEA framework to detect biologically coordinated gene sets — pathways, gene ontology terms, or curated signatures — whose collective expression changes meaningfully over time. Rather than comparing two snapshots, it models the full temporal trajectory of gene expression to identify which functional programs are activated, suppressed, or dynamically remodelled during a biological process such as development, treatment response, or disease progression.

Gene set enrichment method for longitudinal omics data

Time-course RNA-seq or microarray expression matrices with multiple ordered time points

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.

Nueda, M. J., Tarazona, S., & Conesa, A. (2014). Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics, 30(18), 2598–2602.


Time Series Kalman Filter

Kalman Filter for Time Series State-Space Models

state-space Kalman filter
structural time series filter
Kalman smoother for time series
time series state-space filter

1960; time series state-space framework formalised 1980s–1990s

Rudolf E. Kalman; systematised for time series by Andrew Harvey and Søren Johansen

The time series Kalman filter applies the Kalman filtering and smoothing algorithm within a state-space representation of time series models. It recursively extracts unobserved components — trend, seasonality, cycles, and irregular noise — from observed data, providing optimal filtered and smoothed state estimates together with their uncertainty, and enabling exact likelihood evaluation for parameter estimation.

recursive Bayesian filter / state-space smoother

univariate or multivariate time series, equally or irregularly spaced

Durbin, J. & Koopman, S. J. (2012). Time Series Analysis by State Space Methods (2nd ed.). Oxford University Press.

Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.

kalman-filter sequential-monte-carlo particle-filter dynamic-bayesian-network bayesian-regression time-series-bayesian-inference


Time series MCMC

Markov Chain Monte Carlo for Time Series Models

MCMC time series
Bayesian time series MCMC
time series posterior sampling
sequential Bayesian MCMC

Time series MCMC applies Markov chain Monte Carlo methods to Bayesian inference over time-ordered data. Rather than optimising a single parameter estimate, it draws samples from the full joint posterior of parameters and latent states, yielding probability distributions that honestly reflect uncertainty about dynamics, trends, and seasonal patterns across every time point.

Bayesian posterior sampling for time-ordered data

time series (univariate or multivariate, continuous or count)

Carter, C. K. & Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika, 81(3), 541–553.

West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer.

gibbs-sampling sequential-monte-carlo kalman-filter particle-filter dynamic-bayesian-inference hamiltonian-monte-carlo


Time-series metabolomics analysis

longitudinal metabolomics
dynamic metabolomics
temporal metabolome profiling
kinetic metabolomics

Developed from general metabolomics workflows; longitudinal extensions pioneered by A. K. Smilde, R. Bino, and colleagues

Time-series metabolomics analysis profiles small-molecule metabolites from biological samples collected at multiple, ordered time points, enabling researchers to capture the dynamic flux of metabolic pathways in response to stimuli, disease progression, drug treatment, or developmental change. By integrating longitudinal statistical models with standard metabolomics preprocessing, the approach goes beyond a static metabolic snapshot to reveal how, when, and in what sequence metabolic responses unfold.

Quantitative longitudinal omics pipeline

Mass spectrometry (LC-MS, GC-MS) or NMR spectral data collected at multiple time points from the same or matched biological subjects

Smilde, A. K., van der Werf, M. J., Bijlsma, S., van der Werff-van der Vat, B. J. C., & Jellema, R. H. (2005). Fusion of mass spectrometry-based metabolomics data. Analytical Chemistry, 77(20), 6729–6736.

Redestig, H., & Costa, I. G. (2011). Detection and interpretation of metabolite–transcript coresponses using combined profiling data. Bioinformatics, 27(13), i357–i365.


Longitudinal Microbiome Diversity Analysis

longitudinal microbiome diversity analysis

2010s (formalized with 16S amplicon sequencing era; expanded ~2012–2020)

Developed iteratively through the microbiome research community; key contributions from Susan Holmes, Rob Knight, and colleagues

Time-series microbiome diversity analysis tracks how the richness, evenness, and community composition of microbial communities change across multiple time points within the same subjects. By combining standard diversity metrics with longitudinal statistical models, it separates true temporal dynamics from inter-individual variation, identifying when and how perturbations such as diet changes, antibiotic treatment, or disease onset reshape the microbiome.

Longitudinal observational / bioinformatics pipeline

16S rRNA amplicon counts, shotgun metagenomics read tables, OTU/ASV tables across multiple time points

Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583.

Chen, Y., Lun, A. T. L., & Smyth, G. K. (2023). Differential abundance testing on single-cell data using quasi-likelihood methods. Genome Biology, 24(1), 188.


Time series particle filter

Time Series Particle Filter (Sequential Monte Carlo for State-Space Models)

SMC for time series
bootstrap particle filter
sequential importance resampling
particle filtering

The time series particle filter is a Sequential Monte Carlo method that tracks the hidden state of a nonlinear, non-Gaussian state-space model as new observations arrive one at a time. It represents the evolving posterior distribution over the latent state as a weighted cloud of random samples (particles), updating them at each time step through propagation, likelihood weighting, and resampling.

Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107-113.

Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer.

kalman-filter sequential-monte-carlo time-series-bayesian-inference particle-filter time-series-kalman-filter dynamic-bayesian-network


Time-series pathway enrichment analysis

temporal pathway analysis
longitudinal pathway enrichment
dynamic pathway analysis
TPEA

Bar-Joseph and colleagues (temporal gene expression); extended by Cheng, Bhatt et al. for pathway-level time-series inference

Time-series pathway enrichment analysis identifies biological pathways whose coordinated gene activity changes significantly across ordered time points. Rather than treating each time point independently, the method models the temporal trajectory of gene expression within each pathway and tests whether entire biological programs — not just individual genes — are activated or suppressed in a time-dependent manner. It is widely used in developmental biology, drug response studies, and infection time courses.

Functional enrichment analysis with temporal modeling

Time-course RNA-seq or microarray expression data; pathway/gene-set databases (KEGG, Reactome, GO)

Ernst, J., Nau, G. J., & Bar-Joseph, Z. (2005). Clustering short time series gene expression data. Bioinformatics, 21(Suppl 1), i159–i168.

Cheng, J., Tegge, A. N., & Bhatt, D. L. (2014). A method for identifying and interpreting time-series pathway activity changes from gene expression data. Bioinformatics, 30(21), 3147–3154.


Time-series phylogenetic analysis

temporal phylogenetics
time-resolved phylogenetics
molecular clock phylogenetics
phylodynamics

2000s (molecular clock methods earlier; BEAST framework 2007)

Alexei J. Drummond, Andrew Rambaut, and colleagues

Time-series phylogenetic analysis reconstructs the evolutionary history of organisms or genetic variants using sequences sampled at known time points. By incorporating sampling dates directly into the model, it estimates divergence times, substitution rates, and ancestral relationships on an absolute timescale — making it essential for studying viral outbreaks, ancient DNA dynamics, and rapid microbial evolution.

Dated sequence alignments (DNA, RNA, protein) with temporal metadata

Drummond, A. J., & Rambaut, A. (2007). BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology, 7, 214.

Bouckaert, R., Vaughan, T. G., Barido-Sottani, J., Duchene, S., Fourment, M., Gavryushkina, A., et al. (2019). BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLOS Computational Biology, 15(4), e1006650.

phylogenetic-analysis bayesian-phylogenetic-analysis rna-seq-differential-expression genome-wide-association-study sequence-alignment variant-calling


Time-series proteomics analysis

Time-Series Quantitative Proteomics Analysis

longitudinal proteomics
temporal proteomics
dynamic proteomics
time-course proteomics

2000s (quantitative framework: Gygi et al. 1999; time-series designs: 2004–2010)

Multiple groups; Gygi et al. (1999) established quantitative proteomics; time-series designs emerged in the 2000s with LC-MS/MS workflows

Time-series proteomics analysis quantifies protein abundance across two or more ordered time points to reveal how the proteome changes dynamically in response to stimuli, developmental stages, or disease progression. By combining mass spectrometry-based protein quantification with statistical models designed for temporal data, the method identifies proteins with significant expression trends, oscillatory patterns, or delayed responses that cannot be detected in single time-point studies.

Mass spectrometry intensity matrices across ordered time points

Lemeer, S., & Heck, A. J. R. (2012). The phosphoproteomics data explosion. Current Opinion in Chemical Biology, 16(1–2), 1–8.

Ori, A., Iskar, M., Buczak, K., Kastritis, P., Parca, L., Andres-Pons, A., Singer, S., Bork, P., & Beck, M. (2016). Spatiotemporal variation of mammalian protein complex stoichiometries. Genome Biology, 17, 47.


Time-series RNA Sequencing Differential Expression Analysis

longitudinal RNA-seq DE analysis
temporal transcriptomics
time-course RNA-seq
dynamic DE analysis

2006–2018 (principal methods established)

Conesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others

Time-series RNA-seq differential expression analysis identifies genes whose expression levels change systematically across ordered time points — such as during development, disease progression, or response to a treatment. Unlike two-condition DE analysis, it explicitly models the temporal structure of the data, capturing dynamic gene expression trajectories rather than a single snapshot contrast. Tools such as maSigPro, ImpulseDE2, and splineTimeR have been developed specifically for this design.

RNA-seq count matrices from multiple ordered time points

Conesa, A., Nueda, M. J., Ferrer, A., & Talon, M. (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics, 22(9), 1096–1102.

Fischer, D. S., Theis, F. J., & Yosef, N. (2018). Impulse model-based differential expression analysis of time series single-cell RNA-seq data. Genome Biology, 19(1), 1–14.


Time series sequential Monte Carlo

Sequential Monte Carlo Methods for Time Series

particle filter
time series SMC
sequential particle filtering
bootstrap particle filter

Time series sequential Monte Carlo (SMC), commonly called the particle filter, is a Bayesian simulation method that tracks the hidden state of a dynamical system as observations arrive one at a time. A cloud of weighted random samples — particles — is propagated forward through the system dynamics, reweighted by how well each particle explains the new observation, and periodically resampled to keep the representation concentrated on plausible states.

Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F — Radar and Signal Processing, 140(2), 107–113.

sequential-monte-carlo kalman-filter particle-filter bayesian-inference-for-model-comparison dynamic-bayesian-network gibbs-sampling


Time-Series Single-Cell RNA Sequencing Analysis

dynamic single-cell gene expression analysis

2014-2018 (pseudotime and RNA velocity frameworks)

Trapnell et al. (pseudotime/Monocle); La Manno et al. (RNA velocity)

Time-series single-cell RNA-seq analysis captures gene expression across multiple time points at single-cell resolution to reveal how cell populations emerge, transition, and diverge during dynamic biological processes such as development, differentiation, or disease progression. By combining pseudotime ordering, RNA velocity, and differential dynamics testing, researchers reconstruct the temporal trajectory of individual cells and identify the gene regulatory changes that drive biological transitions.

Single-cell RNA sequencing count matrices from multiple time points or with RNA velocity splicing data

Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology, 32(4), 381-386.

La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lonnerberg, P., Furlan, A., Fan, J., Borm, L. E., Liu, Z., van Bruggen, D., Guo, J., He, X., Linnarsson, S., & Kharchenko, P. V. (2018). RNA velocity of single cells. Nature, 560(7719), 494-498.

single-cell-rna-seq-analysis rna-seq-differential-expression pathway-enrichment-analysis gene-set-enrichment-analysis copy-number-variation-analysis eqtl-analysis


Time-series variant calling

longitudinal variant calling
temporal somatic mutation detection
serial variant calling
time-course variant detection

Pioneered in cancer genomics by Nik-Zainal, Campbell, and collaborators (Sanger Institute/Wellcome Trust)

Time-series variant calling is a bioinformatics pipeline that identifies and tracks genomic variants — typically somatic mutations — across multiple sequencing samples collected from the same subject at different time points. It is most widely applied in cancer genomics to reconstruct tumour evolution, monitor minimal residual disease, and detect the emergence of therapy-resistant clones. By jointly modelling variant allele frequencies across the temporal dimension, the method distinguishes true somatic changes from sequencing noise and estimates clonal dynamics over time.

Multi-timepoint next-generation sequencing data (WGS, WES, or targeted panel)

Nik-Zainal, S., et al. (2012). The life history of 21 breast cancers. Cell, 149(5), 994–1007.

McMahon, M., et al. (2021). Benchmarking algorithms for clonal evolution analysis using multi-region and longitudinal tumour sequencing data. Briefings in Bioinformatics, 22(3), bbaa163.

somatic-variant-calling clonal-evolution-analysis copy-number-variation rna-seq-differential-expression single-cell-rna-seq whole-genome-sequencing


Time series variational inference

Variational Inference for Time Series Models

time-series VI
variational Bayes for time series
TSVI
sequential variational inference

Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues

Time series variational inference applies variational Bayes to sequential data, approximating the intractable posterior over latent states and parameters with a tractable family of distributions. By maximising the evidence lower bound (ELBO), it delivers fast, scalable Bayesian inference for state-space models, dynamic latent variable models, and other time-ordered probabilistic systems.

Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877.

Jordan, M. I., Ghahramani, Z., Jaakkola, T. S. & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183-233.

variational-inference sequential-monte-carlo kalman-filter time-series-bayesian-inference dynamic-variational-inference time-series-mcmc


Time-sliced Bibliographic coupling

Time-sliced Bibliographic Coupling Analysis

longitudinal bibliographic coupling
temporal bibliographic coupling
diachronic bibliographic coupling
time-window bibliographic coupling

1963 (base method); time-sliced variant widely adopted 1990s–2000s

Morton M. Kessler (bibliographic coupling); time-sliced extension by various scientometricians

Time-sliced bibliographic coupling divides a publication corpus into successive time windows and applies bibliographic coupling analysis within each window to track how research fronts emerge, shift, merge, or disappear across time. It transforms a static snapshot technique into a longitudinal tool for mapping the intellectual evolution of a scientific field, revealing when and how new thematic clusters appear in the literature.

Longitudinal bibliometric network analysis

Bibliographic records with reference lists, partitioned by publication year or period

Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25.

Glänzel, W., & Czerwon, H. J. (1996). A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level. Scientometrics, 37(2), 195–221.

bibliographic-coupling co-citation-analysis thematic-evolution-analysis science-mapping bibliometric-analysis co-word-analysis


Time-sliced Bibliometric Analysis

longitudinal bibliometrics
temporal bibliometric analysis
diachronic bibliometrics
time-window bibliometric analysis

2000s–2010s (as an explicit methodological variant)

Derived from classical bibliometrics (Price, Garfield); explicitly formalised in longitudinal studies by Zhao & Strotmann (2008) and others

Time-sliced bibliometric analysis partitions a literature corpus into consecutive time windows and applies standard bibliometric indicators (publication counts, citation patterns, co-authorship networks, keyword frequencies) within each window. By comparing results across slices, researchers can document how a field's productivity, intellectual structure, and thematic focus have shifted over time — providing a diachronic rather than static view of scholarly output.

Bibliographic records (titles, abstracts, citations, keywords) partitioned into discrete time windows

Zhao, D., & Strotmann, A. (2008). Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic-coupling analysis. Journal of the American Society for Information Science and Technology, 59(13), 2070–2086.

Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.

bibliometric-analysis scientometric-analysis co-citation-analysis bibliographic-coupling thematic-evolution-analysis science-mapping


Time-sliced Citation analysis

temporal citation analysis
longitudinal citation analysis
time-window citation analysis
diachronic citation analysis

1955–1965 (foundational); temporal slicing formalized in scientometrics from the 1980s onward

Eugene Garfield (citation analysis foundation); Derek J. de Solla Price (temporal/longitudinal framing)

Time-sliced citation analysis partitions a body of literature into sequential temporal windows — for example, five-year intervals — and performs citation analysis within and across each window. This reveals how citation patterns, influential papers, and knowledge flows shift over time, providing a dynamic picture of a field's intellectual evolution rather than a static aggregate snapshot.

Bibliographic citation records with publication date metadata

Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science, 122(3159), 108–111.

Price, D. J. de S. (1965). Networks of scientific papers. Science, 149(3683), 510–515.

citation-analysis bibliometric-analysis co-citation-analysis thematic-evolution-analysis scientometric-analysis bibliographic-coupling


Time-sliced Mapping review

temporal mapping review
time-period mapping review
longitudinal evidence map
chronological mapping review

Campbell Collaboration / Gough, Oliver & Thomas

A time-sliced mapping review is a systematic evidence synthesis that partitions the search period into discrete temporal segments — such as five-year intervals — and constructs a separate evidence map for each slice. By comparing maps across periods, researchers can chart how topics emerge, peak, decline, or transform within a research field, producing a longitudinal picture of knowledge structure that a single-point mapping review cannot provide.

Evidence synthesis with temporal segmentation

Published literature (titles, abstracts, full texts, metadata)

Gough, D., Oliver, S., & Thomas, J. (2012). An Introduction to Systematic Reviews. Sage Publications.

Petrosino, A., Boruch, R. F., Soydan, H., Duggan, L., & Sanchez-Meca, J. (2001). Meeting the challenges of evidence-based policy: The Campbell Collaboration. The Annals of the American Academy of Political and Social Science, 578(1), 14–34.

mapping-review scoping-review systematic-literature-review thematic-evolution-analysis bibliometric-analysis science-mapping


Time-sliced Meta-analysis

temporal meta-analysis
period-stratified meta-analysis
time-segmented meta-analysis
chronological meta-analysis

1992 (cumulative form); refined through 2000s

Lau et al. (cumulative variant); Borenstein et al. (general meta-analytic framework)

Time-sliced meta-analysis is a variant of standard meta-analysis in which the primary studies are partitioned into successive time periods (slices) and a separate pooled effect estimate is computed for each period. By comparing pooled effects across periods, researchers can detect whether an intervention's effectiveness, a relationship's magnitude, or a methodological consensus has shifted over time. This temporal lens transforms a static evidence summary into a longitudinal narrative of how scientific knowledge on a topic has evolved.

Effect sizes (correlations, odds ratios, SMDs) from primary studies stratified by publication period

Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. Wiley.

Lau, J., Antman, E. M., Jimenez-Silva, J., Kupelnick, B., Mosteller, F., & Chalmers, T. C. (1992). Cumulative meta-analysis of therapeutic trials for myocardial infarction. New England Journal of Medicine, 327(4), 248–254.

systematic-literature-review bibliometric-analysis cumulative-meta-analysis thematic-evolution-analysis scoping-review meta-regression-based-meta-analysis


Time-sliced Scientometric Analysis

temporal scientometrics
period-based scientometric analysis
time-window scientometrics
longitudinal scientometric analysis

Derived from scientometrics tradition; temporal slicing formalized in longitudinal bibliometric studies from the 1980s onward

Time-sliced scientometric analysis divides a bibliographic corpus into discrete temporal windows — commonly five- or ten-year periods — and applies standard scientometric indicators (publication counts, citation rates, h-index, collaboration networks, keyword co-occurrence) within each slice. By comparing results across slices, researchers can reconstruct how a scientific field has grown, shifted focus, formed new collaborations, or declined in influence over time. The approach combines the rigor of quantitative scientometrics with an explicit longitudinal dimension.

Bibliographic records (titles, abstracts, citations, author keywords) from scientific databases

Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799-813.

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.

scientometric-analysis bibliometric-analysis thematic-evolution-analysis co-citation-analysis science-mapping co-word-analysis


Time-sliced Systematic literature review

Time-sliced Systematic Literature Review

temporal systematic review
period-based systematic review
chronological systematic review
time-segmented literature review

Adapted from systematic review methodology; temporal segmentation formalized in bibliometric practice (Zupic & Cater, 2015; Aria & Cuccurullo, 2017)

A time-sliced systematic literature review applies the rigorous search, screening, and synthesis protocol of a standard systematic review while dividing the retrieved corpus into discrete temporal periods — time slices — and analyzing each period separately. This design reveals how a research field has developed across time: which topics emerged, grew, or declined; how key authors and journals shifted; and how intellectual structures evolved from one era to the next.

Systematic review variant with temporal segmentation

Bibliographic records, citation data, publication metadata

Zupic, I., & Cater, T. (2015). Bibliometric Methods in Management and Organization. Organizational Research Methods, 18(3), 429–472.