I have a few time series (20+) to be jointly forecasted. They are minute-level data with an obvious weekly seasonality. Therefore the seasonality is 7*24*60=10080.

Typical time series model can not handle such a long seasonality, since it requires a huge number of seasonality parameters (I tried DLM and some similar models).

Most time series models require deseasonality before feeding, is there any way to deal with this long seasonality?

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    $\begingroup$ Here is Rob J. Hyndman's blog post specifically on this topic. Search also for related threads on Cross Validated, something like these. $\endgroup$ – Richard Hardy Jun 10 at 17:06
  • $\begingroup$ Thanks @RichardHardy! That's definitely a good start point. $\endgroup$ – kaixu Jun 10 at 20:50
  • $\begingroup$ I'd be thinking about looking at trigonometric seasonality not dummy seasonality. You can probably get an adequate fit with only a few components. Do you seem to have stationarity across weeks? $\endgroup$ – Glen_b Jun 11 at 3:27
  • $\begingroup$ @Glen_b Yes a fourier series seems to be able to capture the seasonality pretty well. $\endgroup$ – kaixu Jun 11 at 18:19

I would consider possibly using deterministic structure such as 6 dummies for day-of-the-week , 23 dummies for hour-of-the-day and arma structure for short term memory. How to build ARIMA model from my time series? might be of interest


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