Time-sliced Thematic Evolution Analysis

Time-sliced Thematic Evolution Analysis in Bibliometrics

longitudinal thematic mapping
temporal thematic evolution
time-period thematic analysis
diachronic science mapping

Cobo, López-Herrera, Herrera-Viedma & Herrera

Time-sliced thematic evolution analysis is a bibliometric method that divides a corpus of publications into consecutive time windows and tracks how research themes emerge, consolidate, split, merge, or disappear across those periods. By applying co-word analysis and strategic-diagram mapping within each slice and then linking themes across slices, it reveals the intellectual trajectory of a research field over time.

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

Cobo, M. J., López-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., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609–1630.

thematic-evolution-analysis science-mapping co-word-analysis bibliometric-analysis scientometric-analysis systematic-literature-review


Time-varying parameter ADF unit root test

Time-Varying Parameter Augmented Dickey-Fuller Unit Root Test

TVP-ADF test
time-varying ADF
TVP unit root test
adaptive ADF test

Extension of Dickey & Fuller (1979); TVP framework developed across multiple authors including Hall, Psaradakis, and Sola (1997) and Bierens & Martins (2010)

The time-varying parameter ADF (TVP-ADF) test extends the classical Augmented Dickey-Fuller framework by allowing the autoregressive coefficient to evolve over time. Rather than assuming a single fixed unit-root parameter throughout the sample, it models the persistence of a series as a stochastic process, making it sensitive to gradual or episodic changes in stationarity that a standard ADF test would miss.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431.

Hall, S. G., Psaradakis, Z., & Sola, M. (1997). Cointegration and changes in regime: The Japanese consumption function. Journal of Applied Econometrics, 12(2), 151–168.

adf-unit-root-test pp-unit-root-test kpss-stationarity-test rolling-window-regression structural-break-tests time-varying-parameter-model


Time-varying parameter AR model

Time-Varying Parameter Autoregressive Model

state-space AR with drifting coefficients

Cooley & Prescott (1976); further developed by Kim & Nelson (1999) and Cogley & Sargent (2001, 2005)

The Time-Varying Parameter Autoregressive (TVP-AR) model extends the classical AR model by allowing its autoregressive coefficients to drift over time, typically as a random walk. Cast as a state-space system, the model captures gradual structural change in the dynamics of a univariate time series without imposing a fixed break date.

Time-series model with drifting coefficients

Univariate time series (stationary or near-stationary)

Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262-302.

Kim, C.-J., & Nelson, C. R. (1999). State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications. MIT Press.

tvp-var-model state-space-model kalman-filter arima-model stochastic-volatility-model rolling-window-regression


Time-varying parameter ARCH model

Time-Varying Parameter Autoregressive Conditional Heteroscedasticity Model

TVP-ARCH
time-varying ARCH
adaptive ARCH
state-space ARCH

Extension of Engle (1982) ARCH; TVP-ARCH formalization credited to Nicholls & Quinn and subsequent state-space literature

The Time-Varying Parameter ARCH (TVP-ARCH) model extends the classic ARCH framework by allowing both the conditional mean coefficients and the ARCH variance parameters to drift over time according to a random-walk or state-space process. This makes it possible to capture structural shifts in volatility dynamics without imposing a fixed parameter regime.

Conditional heteroscedasticity model with time-varying coefficients

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.

Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262–302.

arch-model garch-model time-varying-parameter-model stochastic-volatility-model kalman-filter egarch-model


Time-varying parameter ARDL bounds test

Time-Varying Parameter Autoregressive Distributed Lag Bounds Test

TVP-ARDL bounds test
time-varying ARDL cointegration
TVP bounds testing approach
dynamic ARDL bounds test

Extension of Pesaran, Shin & Smith (2001); TVP variant developed in applied time-series literature ca. 2010s

The time-varying parameter ARDL bounds test extends the classic Pesaran-Shin-Smith (2001) bounds testing framework by allowing regression coefficients to evolve continuously over time. It detects whether a long-run cointegrating relationship between variables exists and whether that relationship has been stable or shifting across the sample period.

Cointegration / bounds test with time-varying coefficients

Time-series (single equation, possibly I(0)/I(1) regressors)

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.

Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605.

ardl-bounds-test time-varying-coefficient-model nonlinear-ardl rolling-window-regression threshold-cointegration structural-break-test


Time-varying parameter Arellano-Bond GMM

Time-Varying Parameter Arellano-Bond Generalized Method of Moments Estimator

time-varying coefficient dynamic panel GMM

Extension of Arellano & Bond (1991); TVP generalisation developed in panel econometrics literature

The time-varying parameter Arellano-Bond GMM (TVP-AB GMM) is a dynamic panel estimator that extends the classic Arellano-Bond difference GMM framework by allowing regression coefficients to evolve over time. It addresses both individual fixed effects and the endogeneity of lagged dependent variables, while accommodating structural change and parameter instability across the sample period.

Dynamic panel GMM with time-varying coefficients

Balanced or unbalanced panel data (N units, T periods)

Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. The Review of Economic Studies, 58(2), 277-297.

Canova, F., & Ciccarelli, M. (2009). Estimating Multicountry VAR Models. International Economic Review, 50(3), 929-959.

arellano-bond-gmm-estimator panel-arellano-bond-gmm dynamic-panel-data-model time-varying-parameter-var-model panel-system-gmm difference-gmm


Time-varying parameter ARIMA model

Time-Varying Parameter Autoregressive Integrated Moving Average Model

TVP-ARIMA
time-varying ARIMA
adaptive ARIMA
state-space ARIMA

Cooley & Prescott (1976); Harvey (1989) state-space formulation

The time-varying parameter ARIMA model extends the classical ARIMA framework by allowing its autoregressive and moving-average coefficients to evolve over time rather than remaining fixed. Cast in state-space form and estimated via the Kalman filter, it is designed for economic and financial time series whose dynamic structure shifts in response to structural breaks, policy changes, or regime transitions.

Time series model with evolving coefficients

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

Cooley, T. F., & Prescott, E. C. (1976). Estimation in the Presence of Stochastic Parameter Variation. Econometrica, 44(1), 167–184.

arima-model kalman-filter state-space-model time-varying-parameter-model structural-time-series-model regime-switching-model


Time-varying parameter ARMA model

Time-Varying Parameter Autoregressive Moving Average Model

TVP-ARMA
time-varying ARMA
state-space ARMA
locally stationary ARMA

Cooley & Prescott (1976); further formalised by Harvey (1989)

The time-varying parameter ARMA (TVP-ARMA) model extends the classical ARMA framework by allowing the autoregressive and moving-average coefficients to evolve over time. Embedded in a state-space representation and estimated via the Kalman filter, it captures structural change and parameter instability in time series without requiring an explicit breakpoint.

Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167–184.

arma-model state-space-model kalman-filter time-varying-parameter-regression structural-time-series-model locally-stationary-processes


Time-varying parameter DCC-GARCH model

Time-Varying Parameter Dynamic Conditional Correlation GARCH Model

dynamic conditional correlation GARCH with TVP

TVP dynamic conditional correlation model

Robert F. Engle (DCC-GARCH); TVP extension developed in applied finance literature

The TVP-DCC-GARCH model extends the Dynamic Conditional Correlation GARCH framework by allowing not only the pairwise correlations but also the underlying model parameters to evolve continuously over time. It captures structural shifts in volatility dynamics and cross-asset dependence, making it essential for financial risk modelling in non-stationary environments.

Multivariate volatility model with time-varying correlation

Multivariate financial time series (returns, prices)

Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.

Christoffersen, P., Errunza, V., Jacobs, K., & Langlois, H. (2012). Is the potential for international diversification disappearing? A dynamic copula approach. Review of Financial Studies, 25(12), 3711-3751.

dcc-garch-model garch-model multivariate-garch bekk-garch-model dynamic-factor-model stochastic-volatility-model


Time-varying parameter difference GMM

Time-Varying Parameter Difference Generalized Method of Moments

TVP-DGMM
time-varying GMM
TVP difference GMM
dynamic panel TVP estimator

Extends Arellano & Bond (1991) difference GMM; TVP panel extensions developed in the 2000s–2010s literature

Time-varying parameter difference GMM combines the Arellano-Bond first-difference GMM estimator for dynamic panels with a state-space or local-smoothing framework that allows regression coefficients to drift over time. It handles endogeneity and lagged dependent variables while relaxing the assumption that structural relationships remain constant across all periods.

Dynamic panel estimator with time-varying parameters

Panel data (short T, large N) with time-varying structural relationships

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.

Cai, Z. (2007). Trending time-varying coefficient time series models with serially correlated errors. Journal of Econometrics, 136(1), 163–188.

difference-gmm system-gmm time-varying-coefficient-model panel-fixed-effects dynamic-panel-data arellano-bond-estimator


Time-varying parameter dynamic panel data model

Time-Varying Parameter Dynamic Panel Data Model

TVP dynamic panel model
time-varying coefficient panel model
TVP-DPD model
state-space dynamic panel model

Hsiao, Pesaran, and related panel time-series literature

The time-varying parameter dynamic panel data model combines lagged dependent variables with coefficients that evolve over time across panel units. It extends conventional dynamic panel models by allowing slope parameters to shift across periods, making it well-suited for studying structural change, heterogeneous adjustment dynamics, and parameter instability in macro-panels and cross-country datasets.

Dynamic panel model with time-varying coefficients

Balanced or unbalanced panel data with sufficient time dimension (large T)

Canova, F., & Ciccarelli, M. (2009). Estimating multicountry VAR models. International Economic Review, 50(3), 929-959.

Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.

dynamic-panel-data-model random-coefficient-model state-space-model panel-var-model arellano-bond-estimator mean-group-estimator


Time-varying parameter EGARCH model

Time-Varying Parameter Exponential GARCH Model

TVP-EGARCH
time-varying EGARCH
EGARCH with time-varying parameters
dynamic parameter EGARCH

Nelson (1991) for EGARCH; TVP extension developed across the 1990s–2000s literature (e.g., Harvey, Engle and co-authors)

The TVP-EGARCH model extends Nelson's (1991) Exponential GARCH by allowing the volatility equation's parameters — including the leverage effect coefficient — to drift continuously over time. This makes it possible to capture structural change and regime evolution in financial return volatility without imposing a fixed break date.

Financial time series, high-frequency returns

Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.

Harvey, A. C. (2013). Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series. Cambridge University Press.

egarch-model garch-model time-varying-parameter-model gjr-garch-model stochastic-volatility-model markov-switching-garch


Time-varying parameter Engle-Granger cointegration

Time-Varying Parameter Engle-Granger Cointegration Model

TVP Engle-Granger cointegration
time-varying cointegration
TVP-EG cointegration
varying-coefficient cointegration

Engle & Granger (1987) for cointegration; Park & Hahn (1999) for TVP extension

Time-varying parameter (TVP) Engle-Granger cointegration extends the classical two-step Engle-Granger framework by allowing the long-run relationship between integrated series to evolve over time. Instead of assuming a fixed cointegrating vector, the cointegrating coefficients are modelled as stochastic processes — typically via a random walk — and estimated with the Kalman filter or related state-space methods.

Non-stationary time series (I(1) variables)

Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.

Park, J. Y., & Hahn, S. B. (1999). Cointegrating regressions with time varying coefficients. Econometric Theory, 15(5), 664–703.

engle-granger-cointegration johansen-cointegration time-varying-parameter-model error-correction-model state-space-model kalman-filter


Time-varying parameter fixed effects model

Time-Varying Parameter Fixed Effects Model

TVP-FE model
time-varying coefficients fixed effects
TVP panel model
locally time-varying fixed effects

The time-varying parameter fixed effects (TVP-FE) model extends the classical two-way fixed effects panel regression by allowing one or more slope coefficients to change over time while still controlling for unobserved individual heterogeneity. It is used when the effect of a predictor on an outcome is not constant across the time dimension of a panel dataset.

Panel regression with time-varying slopes

Pesaran, M. H., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79-113.

panel-fixed-effects random-effects-model mean-group-estimator state-space-model random-coefficient-model pooled-mean-group-estimator


Time-varying parameter GARCH model

Time-Varying Parameter Generalized Autoregressive Conditional Heteroscedasticity Model

TVP-GARCH
time-varying GARCH
TV-GARCH
state-space GARCH

Engle (1982) for ARCH/GARCH foundation; extended by Creal, Koopman & Lucas (2013) and others for time-varying parameter variants

The Time-Varying Parameter GARCH model extends the standard GARCH framework by allowing the conditional variance parameters — including the ARCH and GARCH coefficients — to change over time rather than remaining fixed throughout the sample. This makes it well-suited to financial and macroeconomic series where volatility dynamics evolve across different market regimes or economic episodes.

Volatility model with time-varying coefficients

Time series (financial returns, macroeconomic aggregates)

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.

Creal, D., Koopman, S. J., & Lucas, A. (2013). Generalized autoregressive score models with applications. Journal of Applied Econometrics, 28(5), 777-795.

garch-model egarch-model state-space-model kalman-filter stochastic-volatility-model rolling-window-regression


Time-varying parameter GLS

Time-Varying Parameter Generalized Least Squares

TVP-GLS
time-varying coefficient GLS
adaptive GLS
state-space GLS

Time-varying parameter GLS extends generalized least squares to settings where regression coefficients are not fixed constants but evolve over time according to a stochastic process. By embedding the model in a state-space framework and applying GLS corrections for non-spherical errors, it captures structural change, regime shifts, and gradually drifting relationships in time-series data.

Time-series regression with drifting coefficients

Time series (continuous outcome and predictors)

Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.

gls-regression kalman-filter state-space-model rolling-ols random-coefficient-model recursive-least-squares


Time-Varying Parameter Granger Causality

TVP Granger causality
rolling-window Granger causality
time-varying Granger test
dynamic Granger causality

C.W.J. Granger (causality concept); TVP extension developed by Primiceri (2005) and subsequent literature

Time-varying parameter Granger causality extends the classical Granger causality framework by allowing the predictive relationships between time series to evolve across time. Instead of assuming fixed causal effects, the model estimates causal coefficients that can shift, capturing structural breaks, regime changes, or gradual evolution in economic or financial relationships.

Time series (macroeconomic, financial, or other sequential data)

Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424-438.

Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821-852.

granger-causality vector-autoregression rolling-regression kalman-filter structural-var time-varying-parameter-var


Time-varying parameter Hausman test

Time-Varying Parameter Hausman Specification Test

Hausman test with time-varying parameters

1978 (Hausman); TVP extension developed through 1980s–2000s

Hausman (1978) specification test framework extended to time-varying parameter settings

The time-varying parameter Hausman test extends Hausman's (1978) classic specification test to models whose coefficients are allowed to evolve over time. It compares an efficient estimator (e.g., OLS or GLS assuming constant parameters) with a consistent estimator from a time-varying parameter model, using the contrast between them to detect parameter instability or endogeneity in dynamic settings.

Time series or panel data with potentially drifting parameters

Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251-1271.

Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167-184.

hausman-test time-varying-coefficient-model random-coefficient-model panel-fixed-effects rolling-window-regression state-space-model


Time-varying parameter Johansen cointegration

Time-Varying Parameter Johansen Cointegration

TVP Johansen cointegration
time-varying cointegration
TVP-VECM cointegration
rolling Johansen cointegration

Johansen (1991) seminal; TVP extension by Park & Hahn (1999) and subsequent literature

Time-varying parameter (TVP) Johansen cointegration extends the classic Johansen framework by allowing the cointegrating vectors and adjustment speeds to evolve over time. It is designed for integrated multivariate time series whose long-run equilibrium relationships are subject to structural change, regime shifts, or gradual parameter drift, common in macroeconomic and financial data.

Multivariate integrated time series (I(1))

Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551–1580.

johansen-cointegration vecm engle-granger-cointegration rolling-window-estimation time-varying-parameter-model threshold-cointegration


Time-varying parameter KPSS test

Time-Varying Parameter Kwiatkowski-Phillips-Schmidt-Shin Test

TVP-KPSS test
time-varying KPSS stationarity test
locally stationary KPSS test
TV-KPSS

Extension of Kwiatkowski, Phillips, Schmidt, and Shin (1992); time-varying generalizations developed by Cavaliere, Taylor, and others

The time-varying parameter KPSS test extends the classic Kwiatkowski-Phillips-Schmidt-Shin (1992) stationarity test to settings where the deterministic or stochastic components of a series may shift over time. It tests the null hypothesis of stationarity while allowing the model's parameters to evolve, making it robust to structural instability that would otherwise distort the standard KPSS result.

Univariate time series with potential structural instability

Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3), 159-178.

Cavaliere, G., & Taylor, A. M. R. (2007). Testing for unit roots in time series models with non-stationary volatility. Journal of Econometrics, 140(2), 919-947.

kpss-test adf-test phillips-perron-test structural-break-unit-root-test time-varying-parameter-model locally-stationary-wavelet-test


Time-varying parameter MA model

Time-Varying Parameter Moving Average Model

TVP-MA model
state-space MA
Kalman filter MA
time-varying MA

Harvey, A. C.; Durbin, J. & Koopman, S. J.

The time-varying parameter moving average (TVP-MA) model extends the standard MA model by allowing the moving-average coefficients to change over time. Cast as a state-space system, it is estimated via the Kalman filter and smoother, making it well suited for series where the shock-transmission dynamics evolve across the sample.

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

time-varying-parameter-ar-model time-varying-parameter-arma-model moving-average-model time-varying-parameter-arima-model arma-model kalman-filter


Time-varying parameter NARDL

Time-Varying Parameter Nonlinear Autoregressive Distributed Lag Model

TVP-NARDL
time-varying NARDL
rolling NARDL
dynamic asymmetric ARDL

Bagnai & Ospina-Rojas (TVP extension); NARDL base by Shin, Yu & Greenwood-Nimmo

The Time-Varying Parameter NARDL (TVP-NARDL) model extends the Nonlinear ARDL framework by allowing the coefficients on positive and negative partial sums of a regressor to change over time. This combination captures both asymmetric responses and structural instability in long-run and short-run relationships within a single cointegrating specification.

Nonlinear time-series model with time-varying coefficients

Time series (single or multiple variables)

Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. Horrace & R. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer.

Bagnai, A., & Ospina-Rojas, C. A. (2019). Time-varying generalisations of the NARDL model. Economics Letters, 177, 73–76.

nardl-model ardl-bounds-test rolling-window-regression time-varying-parameter-model nonlinear-cointegration threshold-regression


Time-varying parameter OLS

Time-Varying Parameter Ordinary Least Squares

TVP-OLS
time-varying coefficient regression
rolling OLS
locally weighted OLS

Cooley & Prescott (1976); further developed by Harvey (1990)

Time-Varying Parameter OLS extends classical ordinary least squares to allow regression coefficients to change over time. Instead of assuming fixed slopes throughout the sample, the model treats each coefficient as a stochastic process, tracking how economic relationships evolve — making it well-suited for analysing structural change in time-series data.

Time-series regression with evolving coefficients

Time-series data (univariate or multivariate)

Cooley, T. F., & Prescott, E. C. (1976). Estimation in the Presence of Stochastic Parameter Variation. Econometrica, 44(1), 167–184.

kalman-filter ols-regression rolling-regression state-space-model panel-fixed-effects structural-break-test


Time-varying Parameter Panel Data Analysis

TVP panel model
time-varying coefficient panel model
state-space panel regression
random coefficient panel model

Cheng Hsiao (panel treatment); Kalman (state-space foundation)

Time-varying parameter (TVP) panel data analysis extends standard panel regression by allowing the slope coefficients to evolve over time for each unit. Instead of assuming a single fixed or random coefficient, the model lets each unit's relationship between predictors and outcome shift period by period, capturing structural change, learning effects, and heterogeneous dynamics across individuals and time.

Balanced or unbalanced panel (cross-section × time series)

Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.

Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45.

panel-fixed-effects panel-random-effects state-space-model kalman-filter random-coefficient-model fama-macbeth-regression


Time-varying parameter PP unit root test

Time-Varying Parameter Phillips-Perron Unit Root Test

Phillips-Perron test with time-varying parameters

Extension of Phillips & Perron (1988); TVP framework attributed to Hall & Luginbuhl (1999) and related literature

The time-varying parameter PP unit root test extends the classical Phillips-Perron test by allowing the autoregressive coefficient to change over time. It detects stochastic non-stationarity in series whose persistence may shift across regimes or periods, offering more reliable inference when structural change is suspected in the data-generating process.

Unit root test with time-varying parameters

Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.

Hall, S. G., & Luginbuhl, R. (1999). Modelling structural breaks in unit root tests using time-varying parameter models. Journal of Economic Dynamics and Control, 23(2), 209-231.

phillips-perron-test augmented-dickey-fuller-test time-varying-parameter-model structural-break-unit-root-test zivot-andrews-test kpss-test


Time-varying parameter quantile-on-quantile regression

Time-Varying Parameter Quantile-on-Quantile Regression

TVP-QQ regression
time-varying QQ regression
dynamic quantile-on-quantile regression
TVP quantile-on-quantile

Extension of Sim & Zhou (2015) QQ framework; TVP adaptation by subsequent applied econometricians

TVP-QQ regression extends the quantile-on-quantile (QQ) framework by allowing the slope coefficients to evolve over time. It maps how the quantiles of a predictor variable affect the quantiles of an outcome differently across the joint distribution and across different time periods, uncovering dynamic, heterogeneous dependence structures that standard regression cannot detect.

Nonparametric time-varying quantile regression

Time-series or panel data; continuous outcome and predictor

Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1–8.

Bouri, E., Gupta, R., & Vo, X. V. (2021). Jumps in geopolitical risk and the cryptocurrency market: The singularity of Bitcoin. Defence and Peace Economics, 33(2), 150–161.

quantile-regression quantile-on-quantile-regression time-varying-parameter-regression rolling-window-regression nonparametric-quantile-regression copula-quantile-regression


Time-varying parameter random effects model

Time-Varying Parameter Random Effects Model

TVP-RE model
random coefficient random effects model
time-varying random effects
TVP panel random effects

The time-varying parameter random effects model extends the classic random effects panel framework by allowing regression coefficients to change over time and across units. Rather than imposing a single fixed slope for all individuals and periods, each coefficient is treated as a random draw that evolves, capturing genuine parameter instability while preserving the random effects assumption that unit-specific components are uncorrelated with the regressors.

Panel regression with time-varying random coefficients

Balanced or unbalanced panel data (cross-sectional units observed over time)

Swamy, P. A. V. B. (1970). Efficient inference in a random coefficient regression model. Econometrica, 38(2), 311–323.

Hsiao, C. (1975). Some estimation methods for a random coefficient model. Econometrica, 43(2), 305–325.


Time-varying parameter SARIMA model

Time-Varying Parameter Seasonal Autoregressive Integrated Moving Average Model

TVP-SARIMA
time-varying SARIMA
state-space SARIMA
adaptive SARIMA

Harvey, A. C.; Durbin, J. & Koopman, S. J. (state-space framework)

The Time-Varying Parameter SARIMA model extends the classical SARIMA framework by allowing autoregressive and moving-average coefficients to evolve over time. Cast as a state-space system and estimated with the Kalman filter, it captures both seasonal patterns and structural change within a single unified model.

Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.

sarima-model state-space-model kalman-filter time-varying-parameter-model arima-model structural-time-series-model


Time-varying parameter SVAR model

Time-Varying Parameter Structural Vector Autoregression Model

TVP-SVAR
time-varying SVAR
drifting-parameter SVAR
TVP structural VAR

The Time-Varying Parameter Structural VAR (TVP-SVAR) model extends classical structural VARs by allowing both the reduced-form coefficients and the structural impact matrix to evolve continuously over time. Estimated via Bayesian MCMC, it captures shifting transmission mechanisms and heteroscedastic volatility — making it the workhorse for empirical macroeconomics when policy regimes and economic relationships change.

Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821–852.

Nakajima, J. (2011). Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications. IMES Discussion Paper Series 2011-E-9, Bank of Japan.

structural-var-model bayesian-var-model time-varying-parameter-var-model factor-augmented-var local-projection-method markov-switching-var


Time-varying parameter system GMM

Time-Varying Parameter System Generalized Method of Moments

TVP System GMM
time-varying System GMM
TVP-SGMM
dynamic panel TVP estimator

1998 (System GMM); TVP extensions in applied literature thereafter

Blundell & Bond (System GMM base); Cooley & Prescott (TVP framework)

Time-Varying Parameter System GMM extends the Blundell-Bond System Generalized Method of Moments estimator to allow regression coefficients to change over time. By combining the instrument-based correction for dynamic endogeneity with a time-varying coefficient structure, the method captures both the persistence of the lagged dependent variable and structural shifts in the effect of regressors across periods.

Dynamic panel estimator with time-varying coefficients

Panel data with dynamic (lagged dependent variable) structure

Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.

Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167–184.


Time-varying parameter TGARCH model

Time-Varying Parameter Threshold Generalized Autoregressive Conditional Heteroscedasticity Model

threshold GARCH with time-varying parameters

Extension combining Zakoïan (1994) TGARCH and time-varying parameter methods

The TVP-TGARCH model extends Threshold GARCH by allowing its volatility parameters to evolve over time via a state-space representation. It captures both the leverage effect — that negative return shocks increase volatility more than positive ones — and structural change in that asymmetry, making it well-suited for long financial time series subject to regime shifts.

Volatility model with asymmetry and parameter evolution

Financial time series (returns, yields, exchange rates)

Zakoïan, 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.

tgarch-model garch-model egarch-model time-varying-parameter-model state-space-model gjr-garch-model


Time-varying parameter Toda-Yamamoto causality

Time-Varying Parameter Toda-Yamamoto Granger Causality Test

1995 (base); TVP variant emerged early 2000s–2010s

Toda & Yamamoto (1995); TVP extension by subsequent applied econometricians

The TVP Toda-Yamamoto causality test combines Toda and Yamamoto's (1995) augmented VAR approach — which handles possibly integrated or cointegrated series without pre-testing for unit roots — with time-varying parameters, allowing causal relationships between variables to shift across different periods rather than remaining fixed throughout the sample.

Time series, possibly nonstationary or integrated

Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.

Adebayo, T. S., & Acheampong, A. O. (2022). Modelling the globalization-emissions nexus: Fresh insights from the novel dynamic ARDL simulations and the Toda-Yamamoto causality approaches. Environmental Science and Pollution Research, 29(3), 3825-3840.

toda-yamamoto-causality granger-causality var-model rolling-window-regression time-varying-parameter-var bootstrap-granger-causality


Time-varying parameter VAR model

Time-Varying Parameter Vector Autoregression Model

TVP-VAR
time-varying VAR
TV-VAR
drifting-coefficient VAR

Primiceri (2005); Cogley & Sargent (2001, 2005)

The Time-Varying Parameter VAR (TVP-VAR) model extends the standard vector autoregression by allowing the coefficients and error covariances to evolve gradually over time. Estimated via Bayesian methods and MCMC simulation, it captures how dynamic relationships between macroeconomic or financial variables shift across different economic regimes without requiring pre-specified break points.

Multivariate time-series model with drifting coefficients

Multivariate macroeconomic or financial time series

Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821-852.

Cogley, T., & Nason, J. M. (1995). Effects of the Hodrick-Prescott filter on trend and difference stationary time series: Implications for business cycle research. Journal of Economic Dynamics and Control, 19(1-2), 253-278.

vector-autoregression structural-var bayesian-var-model time-varying-parameter-ardl-bounds-test state-space-model kalman-filter


Time-varying parameter VECM

TVP-VECM
time-varying VECM
TVP cointegration model
dynamic VECM with drifting coefficients

Park & Hahn (1999); extended by Bierens & Martins (2010)

The Time-Varying Parameter Vector Error Correction Model extends the standard VECM by allowing the adjustment speeds, cointegrating vectors, and short-run dynamics to drift over time. It captures long-run cointegrating relationships among integrated series while accommodating structural change, evolving policy regimes, and shifting economic relationships within a unified state-space framework.

Multivariate integrated (I(1)) time series

Park, J. Y., & Hahn, S. B. (1999). Cointegrating regressions with time varying coefficients. Econometric Theory, 15(5), 664–703.

Vector error correction model. Wikipedia.

vecm tvp-var state-space-model kalman-filter dynamic-ols threshold-cointegration


Time-varying parameter WLS

Time-Varying Parameter Weighted Least Squares

locally weighted time-varying regression

Time-Varying Parameter WLS is a regression technique for time-series data in which the slope and intercept coefficients are allowed to change over time while observations are weighted to account for heteroscedasticity or to discount distant data. It combines the flexibility of state-space coefficient evolution with the variance-correcting power of weighted least squares.

Time-varying coefficient regression with observation weights

Cooley, T. F., & Prescott, E. C. (1976). Estimation in the Presence of Stochastic Parameter Variation. Econometrica, 44(1), 167–184.

kalman-filter-estimation rolling-ols weighted-least-squares state-space-model random-coefficient-model locally-weighted-regression


Time-varying parameter Zivot-Andrews test

Time-Varying Parameter Zivot-Andrews Structural Break Unit Root Test

time-varying Zivot-Andrews unit root test

1992 (base test); TVP adaptation in later applied work

Zivot & Andrews (1992); TVP extension in subsequent applied econometrics literature

The time-varying parameter Zivot-Andrews test extends the classic Zivot-Andrews (1992) structural break unit root test by allowing the regression coefficients to evolve over time. Rather than assuming fixed parameters across the full sample, this approach lets the autoregressive dynamics and break timing adapt through a state-space or rolling framework, improving robustness when economic relationships shift gradually.

Unit root test with endogenous structural break under time-varying parameters

Univariate time series; continuous observations

Zivot, E., & Andrews, D. W. K. (1992). Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270.


Timed Up and Go Test

The Timed Up and Go (TUG) test is a simple, quick performance assessment that measures the time required to stand from a chair, walk 3 meters, turn around, and return to sitting. Developed by Podsiadlo and Richardson in 1991, the TUG has become one of the most widely used tests in geriatric and rehabilitation settings for assessing mobility, balance, and fall risk in older adults and individuals with mobility limitations.

Podsiadlo, D., & Richardson, S. (1991). The timed "Up & Go": A test of basic functional mobility for frail elderly persons. Journal of the American Geriatrics Society, 39(2), 142-148.

Shumway-Cook, A., Brauer, S., & Woollacott, M. (2000). Predicting the probability for falls in community-dwelling older adults. Physical Therapy, 80(9), 896-903.


TimeGPT

Deep Learning, Time Series Forecasting, Foundation Models

TimeGPT is a time series foundation model introduced by Garza and White in 2023 that unifies forecasting, anomaly detection, and classification in a single pre-trained model. Inspired by large language models, TimeGPT is pre-trained on diverse time series and transfers well to downstream tasks with minimal fine-tuning.

Garza, F., & White, C. W. (2023). TimeGPT-1: A Time Series Foundation Model. In ICML 2024 Time Series Workshop.

n-beatsx mamba vision-transformer latent-diffusion-models


Timeline Extraction

Timeline Extraction (Temporal Event Ordering)

Zaman Çizelgesi Çıkarma (Timeline Extraction)

TempEval shared task community (Verhagen et al., 2010)

Timeline extraction is a natural-language-processing task that identifies events mentioned in text, anchors each event to a temporal expression, and arranges them into a chronologically ordered timeline. Formalised through the TempEval shared tasks (Verhagen et al., 2010), it enables automatic reconstruction of historical narratives, news event sequences, and clinical case progressions from unstructured text.

NLP structured information extraction task

Output: Chronologically ordered sequence of events with associated temporal expressions

Verhagen, M. et al. (2010). SemEval-2010 Task 13: TempEval-2. Proceedings of the 5th International Workshop on Semantic Evaluation (ACL).

Minard, A.L. et al. (2016). MEANTIME: MedliNe Annotated TimeLine. Proceedings of the 10th Language Resources and Evaluation Conference (LREC).

named-entity-recognition event-extraction relation-extraction information-extraction text-classification


TimeMixer

TimeMixer (Decomposable Multiscale Mixing)

Decomposable Multiscale Mixing
Multiscale Time-Series Mixer
TimeMixer Model
Çok Ölçekli Zaman Serisi Karıştırıcı

TimeMixer is a decomposition-based, attention-free time-series forecasting architecture introduced by Wang et al. at ICLR 2024. The central idea is to disentangle seasonal and trend components across multiple temporal scales constructed by average pooling, then mix information across those scales using lightweight MLP blocks. By handling coarse (trend-dominant) and fine (seasonal-dominant) resolutions separately and combining their predictions, TimeMixer avoids the quadratic cost of attention while capturing both local and global temporal patterns.

MLP-based multiscale time-series forecasting model

Decomposition + multiscale mixing (no attention)

Wang, S., Wu, H., Shi, X., Hu, T., Luo, H., Ma, L., Zhang, J. Y., & Zhou, J. (2024). TimeMixer: Decomposable multiscale mixing for time series forecasting. ICLR.


TimesFM

Time-series Foundation Model
Google TimesFM
TimesFM forecaster
Zaman Serisi Temel Modeli

TimesFM is a pre-trained foundation model for univariate time-series forecasting introduced by Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou from Google in 2024. The model adopts a decoder-only transformer architecture, similar in spirit to large language models, and is trained on a large corpus of real-world and synthetic time-series data. Its central innovation is the ability to perform accurate zero-shot forecasting across diverse domains without task-specific fine-tuning.

Pre-trained decoder-only transformer for zero-shot time-series forecasting

Large-scale real-world and synthetic time-series datasets

Das, A., Kong, W., Sen, R., & Zhou, Y. (2024). A decoder-only foundation model for time-series forecasting. ICML.


TimesNet

TimesNet (Temporal 2D-Variation Modeling)

Temporal 2D-Variation Network
TimesNet Model
2D Time-Series Network
Zamansal 2B Varyasyon Ağı

TimesNet is a general-purpose time-series model introduced by Wu et al. at ICLR 2023. Its central idea is that univariate or multivariate time series can be reinterpreted as collections of two-dimensional temporal maps by reshaping the 1D signal according to its dominant periodicities, detected via Fast Fourier Transform. This 1D-to-2D transformation exposes both intraperiod patterns (within one cycle) and interperiod trends (across cycles), enabling powerful 2D convolutional architectures to model temporal variation.

1D-to-2D temporal reshaping via FFT-detected periods

Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., & Long, M. (2023). TimesNet: Temporal 2D-variation modeling for general time series analysis. ICLR.


Tinetti

The Tinetti Balance and Gait Assessment (also known as the Performance-Oriented Mobility Assessment or POMA) is a performance-based evaluation tool developed by Mary E. Tinetti in 1986 to assess balance and gait abnormalities in older adults. The battery combines direct observation of balance maneuvers and walking pattern with a systematic scoring rubric, identifying individuals at high risk for falls and functional decline. It is widely used in geriatric clinics, rehabilitation settings, and fall prevention programs as a practical yet sensitive assessment of mobility impairment.

Tinetti, M. E. (1986). Performance-oriented assessment of mobility problems in elderly patients. J Am Geriatr Soc, 34(2), 119-126.

Tinetti, M. E., & Ginter, S. F. (1988). Identifying mobility dysfunctions in elderly patients. Standard neuromuscular tests. J Am Geriatr Soc, 36(1), 55-60.

Tinetti, M. E., Mendes de Leon, C. F., Doucette, J. T., & Baker, D. I. (1994). Fear of falling and fall-related efficacy in relationship to functioning among community-living elders. J Gerontol, 49(3), M140-M147.

short-physical-performance-battery activities-balance-confidence life-space-assessment frail-scale edmonton-frail-scale


THI

Craig W. Newman, Gary P. Jacobson, and James B. Spitzer

The Tinnitus Handicap Inventory (THI) is a 25-item self-report scale that quantifies the functional, emotional, and catastrophic effects of tinnitus on daily life, work, and psychosocial well-being. Developed by Newman, Jacobson, and Spitzer in 1996, it has become the gold-standard outcome measure for assessing tinnitus-related handicap in clinical practice and research. The THI enables clinicians to track disease burden, monitor therapeutic response, and identify patients at risk for severe psychological distress.

Newman, C. W., Jacobson, G. P., & Spitzer, J. B. (1996). Development of the Tinnitus Handicap Inventory. Archives of Otolaryngology - Head & Neck Surgery, 122(2), 143-148.


TiRex

TiRex (xLSTM-based Zero-Shot Forecasting Model)

Time-series xLSTM Forecaster
TiRex Zero-Shot
xLSTM Time-Series Model
Zaman Serisi Sıfır-Atım Tahmincisi

TiRex is a pretrained zero-shot time-series forecasting model introduced in 2025 by the NX-AI xLSTM team (Auer et al.). Built on the Extended Long Short-Term Memory (xLSTM) architecture, TiRex is trained at scale on diverse time-series corpora and can forecast unseen datasets without any fine-tuning. Its core idea is to exploit enhanced in-context learning: the model reads the entire available history as a context and produces forecasts for both short and long horizons directly from that context.

Pretrained zero-shot time-series forecasting model

Large-scale pretraining for zero-shot generalization

Auer, A., Podest, P., Klotz, D., Böck, S., Klambauer, G., & Hochreiter, S. (2025). TiRex: Zero-shot forecasting across long and short horizons with enhanced in-context learning. arXiv preprint.


TLS Protocol Analysis

Transport Layer Security Protocol Specification and Security Assessment

TLS/SSL Protocol
HTTPS Security
Secure Transport Layer

Netscape Communications Corporation, IETF

The Transport Layer Security (TLS) protocol is the cryptographic standard that secures web communication and email transmission. Evolved from SSL (Secure Sockets Layer), TLS provides authentication, encryption, and integrity protection for data in transit. The protocol combines public-key cryptography (RSA, ECDH) for key agreement, symmetric encryption (AES) for bulk data, and digital signatures (SHA-256) for authentication.

Rescorla, E. (2018). The Transport Layer Security (TLS) Protocol Version 1.3. RFC 8446.

Dierks, T., & Rescorla, E. (2008). The Transport Layer Security (TLS) Protocol Version 1.2. RFC 5246.

Bhargavan, K., et al. (2016). FREAK: Factoring RSA Export Keys. Proceedings of the 24th USENIX Security Symposium.

rsa-cryptosystem-analysis diffie-hellman-key-exchange sha-hash-function digital-signature-scheme


TNORM-EINSTEIN

TNORM-EINSTEIN (Einstein T-norm — Einstein product and sum for IFN/PFN aggregation) is a t-norm multi-criteria decision-making (MCDM) method introduced by Klement, E.P.; Mesiar, R.; Pap, E. / Xu, Z.; Yager, R.R. in 1963; 2007. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

T-norm — Einstein product (Hamacher γ=2 special case)

Klement, E.P., Mesiar, R., Pap, E. (2000). Triangular Norms. Kluwer Academic Publishers, Dordrecht

if-topsis if-vikor pf-topsis ivif-topsis


TNORM-FRANK

TNORM-FRANK (Frank T-norm — Frank family of associative t-norms and t-conorms) is a t-norm multi-criteria decision-making (MCDM) method introduced by Frank, M.J. in 1979. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Parametric t-norm family — Frank (generator: log-based)

Frank, M.J. (1979). On the simultaneous associativity of F(x,y) and x+y-F(x,y). Aequationes Mathematicae

if-topsis if-vikor pf-topsis q-rofs-mcdm


TNORM-HAMACHER

TNORM-HAMACHER (Hamacher T-norm — Rational parametric t-norm for fuzzy aggregation) is a t-norm multi-criteria decision-making (MCDM) method introduced by Hamacher, H. in 1978. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Parametric t-norm family — Hamacher (rational generator)

Hamacher, H. (1978). Über logische Verknüpfungen unscharfer Aussagen und deren zugehörige Bewertungsfunktionen. Progress in Cybernetics and Systems Research


TNORM-SCHWEIZER-SKLAR

TNORM-SCHWEIZER-SKLAR (Schweizer-Sklar T-norm — Power-based parametric t-norm family) is a t-norm multi-criteria decision-making (MCDM) method introduced by Schweizer, B.; Sklar, A. in 1960. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Parametric t-norm family — Schweizer-Sklar (power generator)

Schweizer, B., Sklar, A. (1960). Statistical metric spaces. Pacific Journal of Mathematics


Tobit Model

censored regression
limited dependent variable model
Tobit Modeli (Sansürlü Regresyon)

The Tobit model is a regression for outcomes that are censored at a threshold, estimating the relationship by maximum likelihood. Introduced by James Tobin in 1958, it addresses the pile-up of observations at a limit (typically zero) in data such as spending, wages, or duration.

Censored regression (limited dependent variable)

Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24-36.

ols-regression probit-regression logistic-regression quantile-regression negative-binomial-regression


Toda-Yamamoto causality test

Toda-Yamamoto Modified Wald Causality Test

modified Wald test for Granger causality

The Toda-Yamamoto (TY) causality test is a modified Wald procedure for testing Granger causality in vector autoregressions (VARs) estimated in levels, even when variables are nonstationary or cointegrated. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, it restores the standard chi-squared asymptotic distribution of the Wald statistic without requiring prior unit-root or cointegration pretesting.

Time series (possibly nonstationary or cointegrated)

Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.

Dolado, J. J., & Lütkepohl, H. (1996). Making Wald tests work for cointegrated VAR systems. Econometric Reviews, 15(4), 369-386.

granger-causality-test vector-autoregression augmented-dickey-fuller-unit-root-test johansen-cointegration-test vector-error-correction-model arima-model


Toda-Yamamoto Causality

TY Causality Test
Modified Wald Granger Causality
MWALD Test
Toda-Yamamoto Nedensellik Testi

The Toda-Yamamoto (TY) causality test, introduced by Toda and Yamamoto (1995), provides a robust procedure for testing Granger non-causality in vector autoregressive (VAR) models when the variables may be integrated or cointegrated of arbitrary order. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, the method bypasses the need for pre-testing cointegration and preserves the standard asymptotic chi-squared distribution of the Wald statistic.

Intentional lag augmentation avoids pre-testing bias

Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1–2), 225–250.


TODIM

TODIM (TOmada de Decisão Interativa e Multicritério (Interactive and Multicriteria Decision Making)) is a ranking multi-criteria decision-making (MCDM) method introduced by Gomes, L. F. A. M., Lima, M. M. P. P. in 1992. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Prospect theory (loss aversion pairwise dominance)

Gomes, L. F. A. M., Lima, M. M. P. P. (1992). TODIM: Basics and application to multicriteria ranking of projects with environmental impacts. Foundations of Computing and Decision Sciences

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Token Bucket

Token bucket is a simple and elegant algorithm for traffic shaping and rate limiting. A virtual bucket accumulates tokens at a fixed rate (the committed information rate). Incoming packets consume tokens (one token per byte); packets are transmitted only if sufficient tokens are available. If the bucket is full, excess tokens are discarded (no carry-over). Token bucket bounds peak rate and allows controlled bursts, making it ideal for traffic management in networks.

Turner, J. S. (1986). New directions in communications (or which way to the information age?). IEEE Communications Magazine, 24(10), 8-15.

Heinanen, J., Guerin, R., & May, A. (1999). A Single Rate Three Color Marker. RFC 2697.


Tolerance Stack-up

Tolerance Stack-up Analysis and Accumulation Methods

Stack-up analysis
Tolerance accumulation
Geometric tolerance

Tolerance stack-up analysis is a method for predicting the cumulative effect of manufacturing tolerances on the final dimensions and fit of assembled components. When parts with individual tolerances are assembled together, their tolerances combine in complex ways, often producing a result that is worse than each part individually. Stack-up analysis ensures that the final assembly will function correctly despite individual part tolerances, or identifies where tighter tolerances are necessary.

Method for analyzing tolerance accumulation in assemblies

Drake, P. J. (2006). Dimensioning and Tolerancing Handbook (2nd ed.). McGraw-Hill.

Harris, K. (1999). Engineering Tolerance Stack-up and Analysis. Society of Automotive Engineers.

Graves, S. C., & Redfield, C. (2005). Tolerance stack analysis of automotive assemblies. Journal of Manufacturing Science and Engineering, 127(3), 645-652.

design-for-manufacturing-and-assembly cnc-tool-path-generation modal-analysis additive-manufacturing-slicing


Tooth Mobility Assessment

tooth mobility testing
mobility grading
periodontal mobility

Tooth mobility assessment is a clinical examination that evaluates the amount and direction of movement of a tooth when lateral force is applied. Increased tooth mobility indicates loss of periodontal support (bone loss), trauma from occlusion, or other pathology affecting tooth attachment. Systematic mobility grading enables quantification of tooth stability, guides treatment planning, and assesses prognosis in periodontal disease and post-traumatic cases.

Miller, S. C. (1950). Textbook of periodontia (3rd ed.). Philadelphia: The Blakiston Company.

Nulend, J. K., Sloan, A. J., Botero, J. E., et al. (2019). Dentin and pulp-related research: advancing the field. Advances in Dental Research, 31(1), 4-6.

American Academy of Periodontology. (2015). Periodontal disease as a risk factor for systemic disease. Annals of Periodontology, 9(1), 1-1.

periodontal-probing gingival-index occlusal-analysis bone-density-dental


Top Trading Cycles

TTC
Shapley-Scarf Algorithm
Efficient Exchange

Top Trading Cycles (TTC) is an algorithm for allocating indivisible goods to agents such that the allocation is Pareto efficient and individually rational. Developed by Lloyd Shapley and Herbert Scarf in 1974, the algorithm identifies cycles of trades in a preference digraph, executes those trades, and iteratively repeats until no further trades are beneficial. TTC is widely used in kidney exchange and housing allocation due to its efficiency and implementation simplicity.

Shapley, L. S., & Scarf, H. (1974). On cores and indivisibility. Journal of Mathematical Economics, 1(1), 23-37.

Roth, A. E., Sönmez, T., & Ünver, M. U. (2008). Efficient kidney exchange: Coincidence of wants in markets with compatibility. American Economic Review, 97(3), 828-851.

gale-shapley-algorithm vcg-mechanism bayesian-nash-equilibrium principal-agent-model


BERTopic

neural topic modeling
transformer topic modeling
Konu Modelleme — BERTopic

BERTopic is a neural topic-modeling pipeline introduced by Maarten Grootendorst in 2022. It combines BERT-based contextual embeddings with UMAP dimensionality reduction and HDBSCAN clustering to produce coherent, dynamic topics, achieving higher topic coherence than classic topic models.

Transformer embeddings + UMAP + HDBSCAN + class-based TF-IDF

Output: Coherent, interpretable topics with representative terms

Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv:2203.05794.

McInnes, L., Healy, J. & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.


Topic Modeling (LDA)

Latent Dirichlet Allocation Topic Modeling

LDA
latent Dirichlet allocation
Konu Modelleme — LDA

Latent Dirichlet Allocation (LDA) is a generative probabilistic model introduced by Blei, Ng and Jordan (2003) that extracts the hidden topic distributions underlying a collection of documents. It treats each document as a mixture of latent topics and each topic as a distribution over words, turning an unlabelled corpus into interpretable themes.

Output: Latent topic distributions over documents and words

Number of topics k (chosen via coherence score)

Blei, D.M., Ng, A.Y. & Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.

tf-idf word2vec document-clustering sentiment-analysis


NMF Topic Modeling

Topic Modeling with Non-negative Matrix Factorization

non-negative matrix factorization topic modeling

NMF topic modeling uses Non-negative Matrix Factorization — the parts-based decomposition introduced by Lee and Seung (1999) — to extract document-topic distributions from a corpus. By factoring a document-term matrix into two non-negative matrices, it recovers a small set of topics and tends to produce more interpretable topics than LDA.

Non-negative: document-topic and topic-term matrices are all non-negative

Input: TF-IDF document-term matrix

Number of topics k must be set in advance

Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791.

Arora, S., Ge, R., Halpern, Y., Mimno, D., Moitra, A., Sontag, D., Wu, Y. & Zhu, M. (2013). A Practical Algorithm for Topic Modeling with Provable Guarantees. Proceedings of the 30th International Conference on Machine Learning (ICML), 280-288.

topic-modeling-bertopic tf-idf document-clustering bert-embeddings


Topic Modeling

Topic Modeling (Probabilistic Latent Semantic Analysis and Latent Dirichlet Allocation)

Latent Semantic Analysis
probabilistic topic modeling
topic discovery
thematic modeling

Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)

Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.

Unsupervised generative probabilistic model

Text corpora (bag-of-words or token counts)

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

Hofmann, T. (1999). Probabilistic Latent Semantic Analysis. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI), 289–296.

lda-topic-model nmf-topic-model sentence-embeddings bert-based-classification recurrent-neural-network transformer


Topological Deep Learning

TDL
Topological Neural Networks
Higher-Order Deep Learning
Topolojik Derin Öğrenme

Topological Deep Learning (TDL) is a framework that extends deep learning beyond graphs to higher-order topological domains such as simplicial complexes, cell complexes, and hypergraphs. Formalized by Hajij et al. (2023), TDL provides a unified mathematical language for defining message-passing schemes across cells of different ranks, enabling neural networks to model multi-way interactions that pairwise graph edges cannot capture. It is relevant to researchers working with relational, geometric, or biological data exhibiting group-level dependencies.

Topological domains (simplicial, cell, hypergraph complexes)

Hajij, M., et al. (2023). Topological deep learning: Going beyond graph data. arXiv preprint.


Topology Optimization

Topology Optimization (Solid Isotropic Material with Penalization - SIMP)

SIMP
Topology design
Generative design
Density-based optimization

Topology Optimization is a computational method for distributing material optimally within a design space to maximize structural performance (strength, stiffness) while minimizing weight or cost. The Solid Isotropic Material with Penalization (SIMP) method, developed by Bendsoe and Kikuchi (1988), iteratively refines a material density distribution across the design domain using sensitivity analysis and gradient-based optimization. SIMP has revolutionized structural design in aerospace, automotive, and mechanical engineering by automating the generation of efficient structures.

Bendsoe, M. P., & Kikuchi, N. (1988). Generating optimal topologies in structural design using a homogenization method. Computer Methods in Applied Mechanics and Engineering, 71(2), 197-224.

Rozvany, G. I. N. (2009). A critical review of established methods of structural topology optimization. Structural and Multidisciplinary Optimization, 37(2), 217-237.

Deaton, J. D., & Grandhi, R. V. (2014). A survey of structural and multidisciplinary continuum topology optimization: post 2000. Structural and Multidisciplinary Optimization, 49(1), 1-38.

Bendsoe, M. P., & Sigmund, O. (2003). Topology Optimization: Theory, Methods and Applications. Springer.

response-surface-desirability-function finite-element-model-updating first-order-reliability-method rainflow-counting


TOPSIS-SORT

TOPSIS-SORT (TOPSIS-Sort — TOPSIS-based sorting of alternatives into ordered categories) is a sorting multi-criteria decision-making (MCDM) method introduced by Sabokbar, H. F., Hosseini, A., Banaitis, A., Banaitiene, N. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

TOPSIS-based sorting with boundary profiles

Sabokbar, H. F., Hosseini, A., Banaitis, A., Banaitiene, N. (2016). A novel sorting method TOPSIS-SORT: An application for Tehran environmental quality evaluation. E+M Ekonomie a Management


TOPSIS

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications — A State-of-the-Art Survey. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag


Toronto Empathy Questionnaire

Randy Spreng, Mary McKinnon, Raymond Mar, and Brian Levine

The Toronto Empathy Questionnaire (TEQ) is a 16-item self-report measure of empathic ability and emotional responsiveness to others' emotions. Developed by Randy Spreng and colleagues in 2009, the TEQ captures affective empathy—the capacity to feel and share another person's emotions—rather than cognitive perspective-taking. The scale has become widely used in social, clinical, and neuroscience research examining individual differences in emotional empathy and its correlates with mental health, prosocial behavior, and brain structure.

Empathic ability and emotional responsiveness measure

Spreng, R. N., McKinnon, M. C., Mar, R. A., & Levine, B. (2009). The Toronto Empathy Questionnaire: Scale development and initial validation of a factor-analytic solution to multiple empathy measures. Journal of Personality Assessment, 91(1), 62–71.

Eerland, A., Engelen, J. A., & Zwaan, R. A. (2016). The influence of body posture on affective memory. Cognition & Emotion, 30(2), 228–237.

Davis, M. H. (1980). A multidimensional approach to individual differences in empathy. JSAS Catalog of Selected Documents in Psychology, 10, 85.


Toronto Mindfulness Scale

The Toronto Mindfulness Scale (TMS) is a 13-item self-report instrument uniquely designed to measure state mindfulness—the immediate, transient quality of mindful awareness during or immediately following a meditation session. Developed by Zindel V. Segal, Mark A. Lau, and colleagues at the University of Toronto and published in the Journal of Clinical Psychology in 2006, the TMS captures two core dimensions of state mindfulness: Curiosity and Decentering. Unlike trait measures (FFMQ, FMI) which assess habitual mindfulness, the TMS provides moment-to-moment assessment and has become essential in mindfulness-based cognitive therapy (MBCT) and contemplative neuroscience research.

Lau, M. A., Bishop, S. R., Segal, Z. V., Buis, T., Anderson, N. D., Carlson, L., ... & Devins, G. (2006). The Toronto Mindfulness Scale: Development and validation of a state measure of mindfulness. Journal of Clinical Psychology, 62(12), 1445-1467.


Total Productive Maintenance

Total Productive Maintenance (TPM) is a comprehensive maintenance management approach developed by Seiichi Nakajima in the late 1980s that emphasizes employee involvement, preventive maintenance, and continuous improvement to maximize equipment effectiveness. Unlike traditional reactive maintenance, TPM integrates maintenance activities across all organizational levels—from operators to executives—and focuses on eliminating losses (downtime, defects, speed losses) to achieve sustained production efficiency, quality, and safety.

Nakajima, S. (1988). Introduction to TPM: Total Productive Maintenance. Cambridge, MA: Productivity Press.

Wireman, T. (1990). World-class maintenance management. Industrial Press.

kanban smed assembly-line-balancing reliability-block-diagram scor-model


Tourist Loyalty Scale

The Tourist Loyalty Scale (TLS) measures the extent to which visitors intend to return to a destination and recommend it to others, reflecting behavioral commitment and preference relative to competing destinations. Developed by Oppermann (2000) and refined across multiple tourism contexts, the TLS captures the ultimate outcome of satisfaction and destination image—willingness to invest time and money in repeat visitation and endorsement. As the true measure of competitive advantage in tourism, loyalty drives revenue stability, positive reputation, and ecosystem resilience.

Oppermann, M. (2000). Tourism destination loyalty. Journal of Travel Research, 39(1), 78-84.

Getty, J. M., & Getty, R. L. (2003). Lodging quality index (LQI): Assessing Expectations and Perceptions of Lodging Quality. Cornell Hotel and Restaurant Administration Quarterly, 44(2), 33-46.

Reichheld, F. F., & Sasser, W. E. (1990). Zero defections: Quality comes to services. Harvard Business Review, 68(5), 105-111.

Oliver, R. L. (1997). Satisfaction: A Behavioral Perspective on the Consumer. McGraw-Hill.

tourist-satisfaction-scale destination-image-scale travel-motivation-scale place-attachment-scale perceived-value-scale-tourism


Tourist Satisfaction Scale

The Tourist Satisfaction Scale (TSS) measures overall and domain-specific satisfaction of visitors to a destination or tourism facility. Developed across multiple research streams in the 1990s-2000s, it quantifies how well tourism experiences meet visitor expectations across accommodation, attractions, service quality, and value. Essential for destination marketing organizations and hospitality managers seeking systematic feedback on visitor experiences and competitive benchmarking.

Akama, J. S., & Kieti, D. M. (1996). Tourism and socio-economic change in a Kenyan coastal community. Journal of Tourism Studies, 7(2), 45-61.

Spreng, R. A., MacKenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of the determinants of consumer satisfaction. Journal of Marketing, 60(3), 15-32.

Vavra, T. G. (1997). Improving your Measurement of Customer Satisfaction. ASQ Quality Press.

destination-image-scale tourist-loyalty-scale travel-motivation-scale perceived-value-scale-tourism hotel-service-quality-scale


Toxic Leadership Scale

The Toxic Leadership Scale (TLS) is a 16-item instrument measuring destructive leadership behaviors including abusive supervision, narcissism, and authoritarian control. Developed by Einarsen, Aasland, and Skogstad in 2007, the TLS identifies harmful leadership behaviors that undermine organizational effectiveness and employee well-being.

Einarsen, S., Aasland, M. S., & Skogstad, A. (2007). Destructive leadership behaviour: A definition and conceptual model. Leadership Quarterly, 18(3), 207-216.

Shaw, J. B., Erickson, A., & Harvey, M. (2011). A method for measuring destructive leadership and identifying types of destructive leaders in organizations. Leadership Quarterly, 22(4), 575-590.

ethical-leadership-scale authentic-leadership-scale organizational-trust-scale employee-engagement-survey


Tract-Based Spatial Statistics

Tract-Based Spatial Statistics (TBSS) is a voxel-wise analysis method for detecting group differences in white matter microstructure from diffusion MRI data. Published by Stephen M. Smith and colleagues in 2006, TBSS addresses registration and multiple comparison problems inherent in voxel-wise analysis by projecting individual FA maps onto a white matter skeleton derived from a population template.

Diffusion MRI white matter analysis pipeline

Smith, S. M., Jenkinson, M., Johansen-Berg, H., et al. (2006). Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage, 31(4), 1487–1505.

Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381–397.


Traffic Flow (LWR Model)

Lighthill-Whitham-Richards Model for Traffic Flow

LWR model
Traffic wave
Kinematic wave theory

The Lighthill-Whitham-Richards (LWR) model is a macroscopic traffic flow model that treats traffic as a compressible fluid, applying conservation of vehicles and a flow-density relationship. Introduced independently by Lighthill and Whitham (1955) and Richards (1956), the model predicts traffic wave propagation, congestion formation, and bottleneck behavior on highways.

Macroscopic traffic flow modeling using conservation laws

Lighthill, M. J., & Whitham, G. B. (1955). On kinematic waves I. Flow movement in long rivers. Proceedings of the Royal Society A, 229(1178), 281-316.

Richards, P. I. (1956). Shock waves on the highway. Operations Research, 4(1), 42-51.

Daganzo, C. F. (1994). The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research Part B, 28(4), 269-287.


Trail Making Test

TMT
Trails A
Trails B
Trail Making A
Trail Making B

The Trail Making Test (TMT) is a simple, brief neuropsychological test developed by Reitan in 1958 that measures visuomotor processing speed, attention, and executive function. The TMT comprises two forms: Part A, which assesses basic processing speed and visual scanning, and Part B, which assesses executive function, task-switching, and cognitive flexibility. Despite its simplicity, the TMT is highly sensitive to cognitive impairment across a wide range of neurological and psychiatric conditions and remains one of the most widely used screening tests in neuropsychology.

Clinician-administered neuropsychological test of attention and executive function

Reitan, R. M. (1958). Validity of the Trail Making Test as an indicator of organic brain damage. Perceptual and Motor Skills, 8(3), 271-276.

Sanchez-Cubillo, I., Perianez, J. A., Adrover-Roig, D., et al. (2009). Construct validity of the Trail Making Test: Role of task-switching, working memory, inhibition/interference control, and visuomotor abilities. Journal of the International Neuropsychological Society, 15(3), 438-450.

Corrigan, J. D., & Hinkeldey, N. S. (1987). Relationships between parts A and B of the Trail Making Test. Journal of Clinical Psychology, 43(4), 402-409.

frontal-assessment-battery mmse adas-cog cognitive-failures-questionnaire addenbrookes-cognitive-examination


Transaction Management

Database Transaction Management and ACID Properties

Transaction management is the mechanism by which database systems ensure reliable execution of multiple interdependent operations as atomic units. Formalized by Jim Gray and colleagues in the 1980s, transactions guarantee ACID properties (Atomicity, Consistency, Isolation, Durability) that protect data integrity even in the face of failures and concurrent access.

Gray, J. (1981). The transaction concept: Virtues and limitations. VLDB Endowment, 7(6), 519-539.

Papadimitriou, C. H. (1986). The Theory of Database Concurrency Control. Computer Science Press.

Garcia-Molina, H., Ullman, J. D., & Widom, J. (2009). Database Systems: The Complete Book (2nd ed.). Pearson Education.

concurrency-control database-locking isolation-levels deadlock-detection log-based-recovery


STS

The Spiritual Transcendence Scale (STS), developed by Piedmont in 1999, is a 24-item self-report measure of spiritual transcendence: the human capacity to experience connection to something beyond oneself—whether understood as God, nature, humanity, or the sacred. The STS conceptualizes spiritual transcendence as a personality trait distinct from religious adherence or institutional participation, measured through three facets: Prayer Fulfillment (satisfaction from spiritual practices), Universality (sense of interconnection with all people and life), and Connectedness (sense of deep connection to the divine or sacred). The scale has become influential in understanding spirituality as a psychological dimension orthogonal to the Big Five personality traits.

Piedmont, R. L. (1999). Does spirituality have a place in personality science? Journal of Personality and Social Psychology, 76(1), 3–13.

Piedmont, R. L. (2001). Spiritual transcendence as a predictor of psychosocial outcome from an outpatient substance abuse treatment program. Psychology of Addictive Behaviors, 15(4), 338–345.


Transcendental Phenomenology

Transcendental phenomenology, founded by Edmund Husserl, is a qualitative method that seeks the universal essential structures — the invariant essences — of a consciously lived experience. By bracketing all assumptions and prior theories (epoché) and applying eidetic reduction, the researcher uncovers what an experience is in its purest, most fundamental form, independent of any particular context, culture, or individual biography. Clark Moustakas's 1994 adaptation made the method directly accessible to social-science researchers.

In-depth interviews, written first-person descriptions, focus groups (text data)

Moustakas, C. (1994). Phenomenological Research Methods. Sage.

Husserl, E. (1913/1983). Ideas: General Introduction to Pure Phenomenology (F. Kersten, Trans.). Martinus Nijhoff.

phenomenology grounded-theory narrative-analysis case-study thematic-analysis discourse-analysis


Transcultural Self-Efficacy Tool

The Transcultural Self-Efficacy Tool (TSET) is an 83-item self-report measure designed to assess nurses' confidence and capability in delivering culturally competent care. Developed by Jeffreys and Smodlaka in 1996, the TSET evaluates three dimensions of transcultural nursing self-efficacy: cognitive knowledge, practical skills, and affective (emotional) competence. The instrument is widely used in nursing education to evaluate the impact of transcultural curricula and to identify areas requiring additional clinical preparation.

Jeffreys, M. R., & Smodlaka, I. (1996). Construct validation of the Transcultural Self-Efficacy Tool. Journal of Nursing Education, 35(8), 341–348.


Transfer Entropy

Conditional Mutual Information (directed)

Transfer Entropy (TE) is a non-parametric, information-theoretic measure of directed statistical dependence between two time series, introduced by Thomas Schreiber in 2000. Grounded in Shannon entropy, it quantifies how much information the past of one process Y reduces uncertainty about the next state of another process X, beyond what X's own past already provides. Unlike linear correlation or Granger causality, TE captures nonlinear interactions and requires no model assumptions about the underlying dynamics.

Shannon entropy / Kullback-Leibler divergence

Stationary or near-stationary time series

Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464.


Transfer Learning with Diffusion Model

Transfer Learning Applied to Diffusion-Based Generative Models

diffusion model fine-tuning
pre-trained diffusion transfer
TL-DM
domain-adapted diffusion model

Ho et al. (DDPM); transfer application popularized by Rombach et al. (Stable Diffusion) and Ruiz et al. (DreamBooth), 2020–2023

Transfer Learning with Diffusion Models adapts a large pre-trained diffusion model — such as Stable Diffusion or DALL-E 2 — to a new target domain or task by continuing training on a smaller domain-specific dataset. Rather than learning the full generative process from scratch, practitioners leverage knowledge already encoded in millions of training steps to achieve high-quality domain-adapted generation with modest data and compute.

Images, text-image pairs, domain-specific image corpora

Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.

Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2023). DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. CVPR 2023.


Transfer learning GAN

Transfer Learning with Generative Adversarial Networks

TL-GAN
pretrained GAN
GAN fine-tuning
domain-adaptive GAN

Goodfellow, I. et al. (GAN); Wang & Ramanan (transfer to GAN)

Transfer Learning GAN initialises a Generative Adversarial Network — or both its generator and discriminator — from weights pretrained on a large source dataset, then fine-tunes the network on a smaller target dataset. This approach allows high-quality generative modelling even when target-domain data are scarce, by reusing low- and mid-level feature representations learned at scale.

Generative model with transferred weights

Images, text, audio, or any modality supported by the source GAN

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27, 2672–2680.

Wang, Y. & Ramanan, D. (2018). Transferring GANs: generating images from limited data. European Conference on Computer Vision (ECCV), 11205, 220–236.


Transfer Learning Applied to Reinforcement Learning

Transfer RL
TL for RL
cross-task reinforcement learning
inductive transfer in RL

Transfer Learning with Reinforcement Learning (Transfer RL) is a training paradigm in which knowledge acquired by an agent in one or more source tasks — encoded as policy weights, value functions, or learned representations — is reused to accelerate or improve learning in a related but different target task. It directly addresses the sample-inefficiency that plagues reinforcement learning from scratch in complex or expensive environments.

Transfer learning paradigm for sequential decision-making

State-action trajectories, reward signals, environment simulations

Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685.

Lazaric, A. (2012). Transfer in Reinforcement Learning: A Framework and a Survey. In M. Wiering & M. van Otterlo (Eds.), Reinforcement Learning: State-of-the-Art (pp. 143–173). Springer.


Transfer learning variational autoencoder

Transfer Learning with Variational Autoencoder

TL-VAE
pretrained VAE
VAE transfer learning
fine-tuned variational autoencoder

2014 (VAE); 2010 (transfer learning survey)

Kingma, D. P. & Welling, M. (VAE); transfer learning framework from Pan & Yang

Transfer Learning with a Variational Autoencoder (TL-VAE) reuses an encoder and/or decoder pre-trained on a large source dataset and adapts it to a smaller target domain. By inheriting a rich probabilistic latent space rather than starting from random weights, TL-VAE dramatically reduces the amount of target-domain data needed for high-quality generation or representation learning.

Generative model with transferred encoder/decoder

Images, tabular, text, time-series (source and target domain data)

Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014).

Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.


Transfer Learning with BERT-based Text Classification

BERT fine-tuning for classification
BERT transfer learning classifier
pre-trained BERT classifier
BERT downstream classification

2019 (BERT); transfer learning paradigm established circa 2010

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (BERT); Pan, S. J. & Yang, Q. (transfer learning survey)

Transfer Learning with BERT-based Classification adapts a large transformer language model, pre-trained on massive text corpora, to a target classification task by fine-tuning its weights on labeled examples. The pre-trained representations encode rich syntactic and semantic knowledge, enabling high accuracy even when the labeled dataset is small.

Pre-trained transformer fine-tuned for classification

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019, 4171–4186. Association for Computational Linguistics.


Transfer Learning with Convolutional Neural Network (Feature Extraction and Fine-Tuning)

TL-CNN
pretrained CNN
CNN fine-tuning
feature-extracting CNN

Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.

Transfer Learning with CNN reuses a convolutional neural network that has already been trained on a large dataset — most commonly ImageNet — and adapts its learned feature detectors to a new, often smaller target dataset. This lets researchers achieve strong image-recognition performance without the massive compute and data resources required to train a CNN from scratch.

Transfer learning applied to convolutional neural networks

Images, video frames, spectrograms, or any grid-structured input

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NeurIPS), 27, 3320–3328.


Transfer Learning with Graph Neural Network (Pre-trained GNN Fine-tuning)

TL-GNN
pre-trained GNN
GNN transfer learning
graph transfer learning

Hu et al. (GNN-specific); Pan & Yang (transfer learning framework)

Transfer Learning with Graph Neural Networks (GNNs) adapts a GNN pre-trained on a large source graph dataset to a smaller, often label-scarce target graph task. By reusing learned node and edge representations, this approach achieves strong predictive performance where collecting sufficient labeled graph data is expensive or slow — as is common in chemistry, biology, and social network analysis.

Transfer learning / graph representation learning

Graph-structured data (nodes, edges, attributes)

Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., & Leskovec, J. (2020). Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations (ICLR 2020).

Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.


Transfer Learning with Pretrained Deep Neural Networks for Image Classification

pretrained CNN image classification
fine-tuned image classifier
domain-adapted image classifier
TL-IC

Pan, S. J. & Yang, Q. (transfer learning framework); Krizhevsky, Sutskever & Hinton (deep CNN backbone)

Transfer Learning with Image Classification reuses a deep neural network backbone — typically a CNN or Vision Transformer — pretrained on a large dataset such as ImageNet, and adapts it to classify images in a new target domain. By inheriting general visual features from the source task, the approach achieves high accuracy with far fewer labeled images than training from scratch.

Transfer learning / supervised classification

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25.


Transfer Learning with Instance Segmentation

Transfer Learning Applied to Instance Segmentation Networks

transfer learning for panoptic segmentation

2017 (Mask R-CNN); transfer learning paradigm: 2010

He, K. et al. (Mask R-CNN); transfer learning framework: Pan & Yang

Transfer learning with instance segmentation reuses a backbone convolutional network pretrained on a large image corpus (typically ImageNet or COCO) as the feature extractor for an instance segmentation model such as Mask R-CNN, then fine-tunes the full pipeline on a smaller target dataset. This approach delivers state-of-the-art per-object mask accuracy with a fraction of the labeled data and compute that training from scratch would require.

Transfer learning applied to instance segmentation

RGB images (labeled with per-instance masks)

He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.


Transfer Learning with LDA Topic Model

Transfer Learning with Latent Dirichlet Allocation Topic Model

LDA transfer learning
domain-adaptive LDA
knowledge transfer LDA
cross-domain LDA

Transfer Learning with LDA Topic Model applies knowledge from a well-studied source domain to guide Latent Dirichlet Allocation inference on a data-scarce target domain. By injecting source-derived topic priors into the Dirichlet hyperparameters, the method produces coherent, domain-relevant topics even when target-domain text is limited, reducing the volume of labelled or unlabelled data required for meaningful results.

Transfer learning applied to probabilistic topic model

Text corpora (source domain + target domain)

Chen, Z., Mukherjee, A., Liu, B., Hsu, M., Malas, M., & Wang, S. (2013). Leveraging multi-domain prior knowledge in topic models. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI-13), pp. 2071–2077.

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

lda-topic-model transfer-learning-with-nmf-topic-model domain-adaptive-lda-topic-model fine-tuned-lda-topic-model topic-modeling multilingual-lda-topic-model


Transfer Learning with LSTM

Transfer Learning with Long Short-Term Memory Networks

LSTM Transfer Learning
Pre-trained LSTM
LSTM Fine-Tuning
ULMFiT-style LSTM Transfer

Howard, J. & Ruder, S. (ULMFiT); general concept: Pan & Yang (2010)

Transfer Learning with LSTM is a technique in which a Long Short-Term Memory network is first pre-trained on a large source corpus or task, and then its learned weights are transferred and fine-tuned on a smaller target task. This approach, popularized by ULMFiT (Howard & Ruder, 2018), allows LSTM-based models to reach strong performance even when labeled target data is scarce.

Sequential text, time-series, speech, or other sequence data

Howard, J. & Ruder, S. (2018). Universal Language Model Fine-Tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 328–339.


Transfer Learning with Named Entity Recognition (Pretrained Encoder Fine-Tuned for NER)

TL-NER
Fine-Tuned NER
Pretrained Model NER
BERT NER

Pan & Yang (transfer learning); Devlin et al. (BERT-based NER fine-tuning)

Transfer Learning with Named Entity Recognition (NER) adapts a large pretrained language model — such as BERT, RoBERTa, or a domain-specific encoder — to the task of identifying and classifying named entities (persons, locations, organizations, dates, etc.) in text. By reusing rich linguistic representations learned from massive corpora, this approach requires only modest labeled NER data while achieving state-of-the-art span detection and classification accuracy.

Supervised sequence labeling via pretrained encoder fine-tuning

Text (token sequences with entity labels)

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.


Transfer Learning with NMF Topic Model

Transfer Learning with Non-Negative Matrix Factorization Topic Model

TL-NMF
NMF transfer topic model
cross-domain NMF topic modeling
domain-adaptive NMF

2010 (transfer learning survey); 1999 (NMF)

Pan, S. J. & Yang, Q. (transfer learning framework); Lee, D. D. & Seung, H. S. (NMF base)

Transfer Learning with NMF Topic Model applies knowledge from a labeled or data-rich source domain to improve Non-Negative Matrix Factorization topic discovery in a low-resource target domain. By initializing or constraining the NMF basis matrix with source-domain topics, the model discovers coherent target topics even when target-domain documents are scarce or unlabeled.

Unsupervised topic model with cross-domain adaptation

Text corpora (document-term matrices); source and target domain documents

Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.

nmf-topic-model lda-topic-model transfer-learning-with-lda-topic-model transfer-learning-with-transformer domain-adaptive-nmf-topic-model topic-modeling


Transfer Learning with Object Detection

Transfer Learning Applied to Object Detection

pretrained object detector
fine-tuned object detection
TL-OD
domain-adapted object detection

Girshick, R. et al. (R-CNN line); Pan & Yang (transfer learning framework)

Transfer learning with object detection starts from a deep neural network pretrained on a large image dataset — typically ImageNet for the backbone or COCO for the full detector — and adapts it to detect objects in a new domain. By reusing learned visual representations, it achieves strong detection accuracy with far fewer annotated images than training from scratch would require.

Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems (NeurIPS), 28.


TL-RNN
Pretrained RNN
RNN Transfer Learning
Recurrent Transfer Learning

Pan, S. J. & Yang, Q. (transfer learning survey); RNN origins: Rumelhart, D. E. et al. (1986)

Transfer Learning with Recurrent Neural Network (TL-RNN) reuses weights learned by an RNN on a large source task — such as language modelling or sequence prediction — and adapts them to a new, often smaller target task. This strategy lets practitioners obtain strong sequence-modelling performance without the need for massive labelled datasets.

recurrent-neural-network long-short-term-memory gated-recurrent-unit fine-tuned-recurrent-neural-network transfer-learning-with-lstm transfer-learning-with-transformer


Transfer Learning with Pre-trained Sentence Embedding Models

pre-trained sentence encoder fine-tuning

Reimers, N. & Gurevych, I. (SBERT); Conneau, A. et al. (InferSent)

Transfer Learning with Sentence Embeddings takes a large pre-trained encoder — such as Sentence-BERT or the Universal Sentence Encoder — that already encodes general language knowledge into fixed-length vectors, and adapts it to a new task or domain with little additional labelled data. The pre-trained representations give a head start that often outperforms task-specific models trained from scratch on modest corpora.

Transfer learning / sentence representation

Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3982–3992.

Conneau, A., Kiela, D., Schwentz, H., Barrault, L. & Bordes, A. (2017). Supervised Learning of Universal Sentence Representations from Natural Language Inference Data. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), 670–680.


Transfer Learning with Neural Text Summarization

neural abstractive summarization via transfer learning

Transfer Learning with Text Summarization adapts a large language model pre-trained on broad text corpora — such as T5, BART, or PEGASUS — to the task of condensing documents into shorter, coherent summaries. By reusing learned linguistic knowledge and fine-tuning on domain-specific pairs of source documents and reference summaries, this approach achieves strong summarization quality with modest labeled data requirements.

Transfer learning applied to sequence-to-sequence summarization

Text corpora with reference summaries (e.g., news articles, scientific abstracts)

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.

Lewis, M., Liu, Y., Goyal, N., Ghahravi, M., Mohamed, A., Chen, D., Levy, O., & Zettlemoyer, L. (2020). BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 7871–7880). ACL.


Transfer Learning with Topic Modeling

Transfer Learning with Topic Modeling (Cross-Domain Topic Adaptation)

domain-transfer topic modeling
pretrained topic transfer
cross-domain topic adaptation
TL-LDA

Pan, S. J. & Yang, Q. (transfer learning survey); combined with Blei et al. (LDA, 2003)

Transfer Learning with Topic Modeling adapts topic structures discovered on a large or well-labeled source corpus to a related but distinct target domain where labeled data or large corpora are scarce. By reusing source-domain topic priors or pretrained embeddings as initialization, the approach produces richer, more coherent topics in the target domain than training from scratch.

Text corpora (source domain + target domain documents)

lda-topic-model nmf-topic-model fine-tuned-topic-modeling domain-adaptive-topic-modeling sentence-embeddings bert-based-classification


Transfer Learning with Word2Vec

Transfer Learning with Word2Vec Pre-trained Embeddings

Word2Vec transfer learning
pre-trained Word2Vec embeddings
Word2Vec embedding initialization
Word2Vec fine-tuning

Mikolov, T. et al. (Word2Vec); transfer-learning application popularised by Kim, Y.

Transfer Learning with Word2Vec uses word embeddings pre-trained on large text corpora via the Skip-gram or CBOW objectives introduced by Mikolov et al. (2013) to initialize the embedding layer of a downstream NLP model. This approach transfers distributional semantic knowledge to tasks where labeled data is scarce, consistently outperforming random initialization.

Transfer learning / embedding initialization

Text (tokenized natural language corpora)

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems (NIPS), 26, 3111-3119.

Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1746-1751.


Transfer Learning

Transfer Learning (Domain Adaptation and Knowledge Transfer)

TL
domain adaptation
fine-tuning
pre-trained model adaptation

Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)

Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.

Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.

Bengio, Y. (2012). Deep Learning of Representations for Unsupervised and Transfer Learning. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning, PMLR 27, 17–36.

self-supervised-learning few-shot-learning semi-supervised-learning fine-tuning meta-learning domain-adaptation