TSML.jl: a package for time series data processing, classification, clustering, and prediction written in Julia

Package Features:

• Support for symbolic pipeline composition of transformers and learners
• TS data type clustering/classification for automatic data discovery
• TS aggregation based on date/time interval
• TS imputation based on symmetric Nearest Neighbors
• TS statistical metrics for data quality assessment
• TS ML wrapper with more than 100+ libraries from caret, scikitlearn, and julia
• TS date/value matrix conversion of 1-D TS using sliding windows for ML input
• Common API wrappers for ML libs from JuliaML, PyCall, and RCall
• Pipeline API allows high-level description of the processing workflow
• Specific cleaning/normalization workflow based on data type
• Automatic selection of optimised ML model
• Automatic segmentation of time-series data into matrix form for ML training and prediction
• Easily extensible architecture by using just two main interfaces: fit and transform
• Meta-ensembles for robust prediction
• Support for threads and distributed computation for scalability, and speed