PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series

Here are updates,

1. for missing values after LOCF imputation (that are missing since the first step hence LOCF doesn't work), we added more options to handle them. Please refer to the argument first_step_imputation in LOCF docs. The default option is "zero" in previous versions, but we've changed it to "backward" which is more reasonable;
2. enabled SAITS to return latent attention weights from blocks in predict() for advanced analysis e.g. in #178;
3. renamed model saving and loading functions save_model() and load_model() into save() and load();

What's Changed

• Check if X_intact contains missing data for imputation models, check and list mismatched hyperparameters in the tuning mode by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/234
• Make SAITS return attention weights in predict() by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/239
• Adding other options for the first step imputation in LOCF by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/240
• Fixing the problem about staling issues by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/244
• Testing with Python 3.11 and support it by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/246
• Rename save_model() and load_model() into save() and load() by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/247
• Refactoring save_model() and load_model(), and updating docs by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/249

Full Changelog: https://github.com/WenjieDu/PyPOTS/compare/v0.2...v0.2.1