PyPOTS VS SAITS

You're talking about the end-to-end task on partially-observed time series (POTS) like classification. But sometimes people need to know approximated values of missing parts. Imputation works for them. And imputation can help with downstream tasks (e.g. classification) as well, please refer to the section “4.4.2 Downstream Classification Task” in the SAITS paper. BTW, we include end-to-end methods for modeling POTS data in PyPOTS that may interest you, this library supports four tasks of classification, forecasting, clustering, and imputation.

GitHub repo: https://github.com/WenjieDu/PyPOTS

Oh, wow, thanks for sharing it here! PyPOTS still has a long way to go, and I'm making it better. If you have any suggestions for PyPOTS, please let me know. Your feedback is always welcome and means a lot to the community of PyPOTS! If you like PyPOTS, please star 🌟 PyPOTS repo on GitHub and share it with people you know who may need it to help others notice this helpful work. Thank you very much!

Due to all kinds of reasons like failure of collection sensors, communication error, and unexpected malfunction, missing values are common to see in time series from the real-world environment. This makes partially-observed time series (POTS) a pervasive problem in open-world modelling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated toolkit. PyPOTS is created to fill in this gap. PyPOTS (pronounced "Pie Pots") is 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. It is born to become a handy toolbox that is going to make data mining on POTS easy rather than tedious, to help engineers and researchers focus more on the core problems in their hands rather than on how to deal with the missing parts in their data. PyPOTS will keep integrating classical and the latest state-of-the-art data mining algorithms for partially-observed multivariate time series. For sure, besides various algorithms, PyPOTS has unified APIs together with detailed documentation and interactive examples across algorithms as tutorials. Feedback and contributions are very welcome! Website: https://pypots.com Paper link: https://arxiv.org/abs/2305.18811 GitHub repo: https://github.com/WenjieDu/PyPOTS

Website: https://pypots.com

The full paper is available on arXiv: https://arxiv.org/abs/2202.08516. The code on GitHub: https://github.com/WenjieDu/SAITS/. If your research lies in time-series modeling, you may also be interested in the work PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series. Its full paper is available on arXiv as well https://arxiv.org/abs/2305.18811, which has been peer-reviewed and accepted by the 9th SIGKDD international workshop Mining and Learning from Time Series (MiLeTS'23).

Missing values in time series collected from the real world are common to see and very pesky. A new state-of-the-art and fast neural network called "SAITS“ is proposed to help impute missing data in partially-observed multivariate time series. The paper has been peer-reviewed and published in the journal Expert Systems with Applications (DOI link). The full paper is available on arXiv at this URL. The code is open source on GitHub https://github.com/WenjieDu/SAITS/.

The code on GitHub: https://github.com/WenjieDu/SAITS/.