PyPOTS: A Python Toolbox to Ease Loading Open-Source Time-Series Datasets, https://github.com/WenjieDu/PyPOTS, https://arxiv.org/abs/2305.18811, the first (and so far the only) Python toolbox/library specifically designed for data mining and machine learning on partially-observed time series (POTS), namely, incomplete time series with missing values, A.K.A. irregularly-sampled time series, supporting tasks of imputation, classification, clustering, and forecasting on POTS datasets. PyPOTS has achieved more than 300 stars.
TSDB (Time Series Data Base): A Python Toolbox to Ease Loading Open-Source Time-Series Datasets, https://github.com/WenjieDu/TSDB, supporting 119 open-source datasets so far.
cuge1995
commented
1 year ago
SAITS: Self-Attention-based Imputation for Time Series, a state-of-the-art imputation model for time series, https://arxiv.org/abs/2202.08516, accepted by the journal Expert Systems with Applications, DOI link https://doi.org/10.1016/j.eswa.2023.119619
PyPOTS: A Python Toolbox to Ease Loading Open-Source Time-Series Datasets, https://github.com/WenjieDu/PyPOTS, https://arxiv.org/abs/2305.18811, the first (and so far the only) Python toolbox/library specifically designed for data mining and machine learning on partially-observed time series (POTS), namely, incomplete time series with missing values, A.K.A. irregularly-sampled time series, supporting tasks of imputation, classification, clustering, and forecasting on POTS datasets. PyPOTS has achieved more than 300 stars.
TSDB (Time Series Data Base): A Python Toolbox to Ease Loading Open-Source Time-Series Datasets, https://github.com/WenjieDu/TSDB, supporting 119 open-source datasets so far.