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

Data scientist working in the financial services industry