# Produce a forecast based upon multiple time series with variable lag

Firstly, I'm not a data scientist, but I am keen to understand the power of the subject and have invested some time in learning - most examples of time series analysis, however, consider only a single date series.

I'm looking for advice on the most appropriate methods to learn/practice to allow me to develop a signature (prediction) of how one time-lagging variable affects another. The lag of cause/effect of one variable upon the other is variable depending on river flow, but likely to be between 2 and 8 hours.

I would need to firstly demonstrate some evidence of correlation, accounting for the lag which I am able to calculate in any given circumstance, and then a mechanism of forecasting.

The intention is to ultimately have a system automatically respond to a given situation by preparing in advance for an upstream change by reference to a signature, which would otherwise have reacted to the change as it arrived (and thus less effectively).

Any prompts in the right direction appreciated.

You could look into neural nets, e.g. using LSTM layers. There are examples, such as „Jena“ weather forecast with Keras: