tslearn and tsml-eval
tslearn provides a comprehensive machine learning toolkit with algorithms and transformers for time series, while tsml-eval focuses specifically on standardized benchmarking and evaluation of those algorithms—making them complements that serve different stages of the ML pipeline.
About tslearn
tslearn-team/tslearn
The machine learning toolkit for time series analysis in Python
This toolkit helps data scientists and machine learning engineers analyze sequential data by providing specialized algorithms for time series. You input raw time series data, and it helps you preprocess, classify, cluster, or predict trends within that data. It's designed for practitioners who work with data that changes over time, such as sensor readings, stock prices, or patient vitals.
About tsml-eval
time-series-machine-learning/tsml-eval
Evaluation tools for time series machine learning algorithms.
This tool helps machine learning researchers and data scientists compare the performance of different time series algorithms. You input various time series datasets and the algorithms you want to test, and it outputs detailed evaluation metrics, showing which algorithms perform best. It's designed for those who develop or rigorously test new time series models.
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