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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.

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

Alternatively, you can use VAR models (vector autoregressive models). If you use R, have a look here (section "Multivariate Time Series Models").

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  • $\begingroup$ These are excellent references: thank you, exactly what I was after. $\endgroup$ Nov 23 '21 at 22:46

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