There are a lot of resources for time series forecasting using neural networks. Because neural networks are so flexible, you can have it produce multiple outputs [x1, …, xt] or forecasts in the future. It requires a custom cost function that aggregates the loss for each prediction.

LSTM’s are popular for forecasting time series — this machine learning mastery tutorial is an example:

Current cutting edge (to my knowledge) is to use dilated CNN’s for forecasting. These are much faster to train than LSTM’s.

Data scientist working in the financial services industry

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