Time Series Library (TSLib) TSLib is an open source library for deep learning researchers, especially for deep time series analysis. We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: long and short term forecasting, imputation, anomaly detection, and classification. This benchmark collection is designed to evaluate and develop advanced deep time series models. For an in depth exploration of current time series models and their performance, please refer to our paper Deep Time Series Models: A Comprehensive Survey and Benchmark . To get started with the codebase and contribute, please visit the GitHub repository . Dataset Overview Tasks Benchmarks Metrics Series Length Forecasting Long term: ETT (4 subsets), Electricity, Traffic, Weather, Exchange, ILI MSE, MAE 96\~720 (ILI: 24\~60) Short term: M4 (6 subsets) SMAPE, MASE, OWA 6\~48 Imputation ETT (4 subsets), Electricity, Weather MSE, MAE 96 Classification UEA (10 subsets) Accuracy 29\~1751 Anomaly Detection SMD, MSL, SMAP, SWaT, PSM Precision, Recall, F1 Score 100 File Structure Usage You can load the dataset directly using the datasets library: Or dow…
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