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