2
$\begingroup$

I have three different linear, multi-variate time series models with a best fit against the same observed value $Y$ at 1 minute, 3 minutes and 10 minutes horizons respectively. Each model is using different predictors data. There is no serial correlation or ARIMA involved here.

I want to blend the three models to predict $Y$ every second, on a rolling base. The prediction for the three models is thus overlapping. For example, at time $t_3$ minutes, we'll have the prediction for the first and second model colliding (third prediction for the 1 minute model and first for the 3 minutes model).

How should I approach the problem?

$\endgroup$

1 Answer 1

4
$\begingroup$

Your problem looks like it's related to looking at time series aggregates on different frequencies. That is, one could look at daily sales data, build a model on that, forecast daily data... then aggregate historical data to weeks, build a model on weekly data and forecast weekly data... finally do the same exercise after aggregating to monthly data. Then finally one would combine the forecasts on different frequencies - and this last step seems to be what you are mainly interested in.

There is a recent very nice paper on the whole procedure by Kourentzes, Petropoulos and Trapero. I imagine that you could profit from looking at the final combination step in the paper.

$\endgroup$
1
  • $\begingroup$ That's a very good resource, thank you. In my case the models are based on different predictors data (not just different aggregation periods), but it looks like the methodology is still relevant. $\endgroup$ Aug 20, 2014 at 12:34

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.