Frequently Asked Questions

Inclusion Criteria

Similarly to scikit-learn, PyPOTS mainly considers well-established models/algorithms for inclusion. A rule of thumb is the paper should be published for at least 1 year, have 20+ citations, and the usefulness to our users can be claimed.

However, we encourage the authors of proposed new models to share and add your implementations into PyPOTS to help boost research accessibility and reproducibility in the field of POTS modeling. Note this exception only applies if you commit to the maintenance of your model for at least two years, and you should contact Wenjie Du to discuss the details of your implementation and maintenance plan before coding start.

Our Development Principles

  1. Do one thing and do it well (Unix philosophy). We’re PyPOTS: we don’t build everything related to time series and we don’t have to, but only things related to partially-observed time series. And when we build something in PyPOTS, we’re responsible and trying our best to do it well;

  2. Eat our own dog food. We develop PyPOTS and we should try the best to use it in any scenarios related to POTS data. Only in this way, we can figure out how it tastes like, if it is a good toolset for users, and what other features and models should be included into PyPOTS;

  3. No silver bullet and No free launch. There is no one solution to all problems in the universe. In PyPOTS, we keep things modular, so one can easily try and replace parts of the pipeline in search for the optimal combination for the particular task;

  4. Keep things easy to use and familiar. We try to keep PyPOTS intuitive without compromising flexibility and without forcing users to learn a completely new technology. We do this by keeping the toolkit close to APIs in scikit-learn and pytorch that people know and love;