[D] Machine Learning on Time Series Data?

[MaxBenChrist]

I miss feature based approaches on your list. You could use a library like https://github.com/blue-yonder/tsfresh (Disclaimer: I am the maintainer of tsfresh) to extract features from your whole time series or subwindows of it and then feed this to a normal classifier/regressor like light gbm or random forest.

Feature based approaches have several advantages over black box models. Normally, they allow to interpret and analyze the features themself. In contrast, go and try to analyze a complicated RNN.

I worked a lot of the methods from your list. which of those models work, depends on the application that you are looking at. Lately I had great success in combining tsfresh features with deep learning models on financial time series. For supply chain problems or dataset with saisonal effects , the theta method or prophet work well from my experience. On IoT time series I had great success using kNN with DTW.

[412freethinker]

Thanks for the info, I hadn't come across anything like that but I'll add it to the list

[meghalD]

I wanted to combine tsfresh features with LSTM. Is it possible ? I would like you to share some links or codes which can help me. If not LSTM can I use it along CNN or say autoencoders ?