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I have a large set of relatively simple time series with very similar behaviour, on which I would like to do short-term forecasts. These series are non-aligned, and at one moment in time, only a small fixed number of series is considered. After that, the series are dropped and never become relevant again.

An example to clarify:

At 12:00, three series [A, B, C] are considered, and each is forecasted using an instance of the generic model.

At 12:05, three series [B, C, D] are considered, and each is forecasted with its instance of the generic model. D being a series never seen by the model before. It is a very similar series to the previously seen, and I would expect the model to generalize and provide a reasonable forecast.

I am essentially looking for a model that I could fit on multiple time series and then create instances with data from different series and get forecasts.

What models could suit this problem well? How can I fit/train the model on all the training series evenly to not re-fit just for the last observed series?

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I suspect that exponential smoothing models would do what you want, but I am not sure exactly what you are asking. They are very easy to run and forget data past a specific point - well they weight data heavily depending on what you tell it.

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  • $\begingroup$ Thanks for the suggestion. I think I found what I am looking for, the key was redefining the problem as a regression problem. The similar nature of the series allows even with the time series information "lost" to provide decent results and generalize well over multiple series. $\endgroup$ – holyfish Mar 4 at 10:57

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