What are some ways to deal with large volumes of variable-length timeseries for real-time predictions? The best solutions I've tried myself all hinge on windowed-feature extraction or LSTMs. It generally works, but starts to fall apart when you're squeezed for data.
It seems that almost everywhere you look, every example has just one timeseries that needs to be dealt with. However, since the methods are much more "statistical" in nature, they can actually make meaningful predictions on a single sample.
For training forecasting models on multiple time series (and potentially large datasets), you can take a look at Darts [1] and the blog post [2].
[1]: https://github.com/unit8co/darts/
[2]: https://medium.com/unit8-machine-learning-publication/traini...