I want to create forecasting for a large quantity of time series. Since they are too many, I am thinking on reducing my data by clustering it into to similar groups. However, I am using SPSS modeler and it is not possible to cluster time series (only static data).
Do you think it makes sense to apply clustering on static data and fit the forecasting model on its centroide?
Which software do you suggest to cluster the time series directly?
Thanks!
Best Answer
"Dynamic factor analysis" might be your answer and is certainly worth reading up on. It aims to identify a small number of underlying (unobserved) factors behind the co-movement of a large number of observed time series. To implement as part of a broader modelling exercise you will probably be looking at R, Matlab, Stata or a specialist time series package like RATS.
There are lots of potential pitfalls, however, including the possibility that in reducing the explanatory variables in your data to the underlying common "structure", you may be throwing away precisely that part of it which is related to your response variable of interest. However, the technique seems widely used, particularly in areas like econometrics that can try to combine it with a theoretical sense of what the structure underneath all those time series may be.