Add SeFT as a classification model

WenjieDu / PyPOTS

A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values

BSD 3-Clause "New" or "Revised" License

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1. Model description

SeFT as a classification model for POTS should be included in PyPOTS.

@InProceedings{pmlr-v119-horn20a,
title = {Set Functions for Time Series},
author = {Horn, Max and Moor, Michael and Bock, Christian and Rieck, Bastian and Borgwardt, Karsten},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {4353--4363},
year = {2020},
editor = {Hal Daumé III and Aarti Singh},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
}

2. Check open-source status

[X] The model implementation is publicly available

3. Provide useful information for the implementation

https://github.com/BorgwardtLab/Set_Functions_for_Time_Series

WenjieDu / PyPOTS