I would like to separate a big time series into its components to improve forecasting.
To clarify: I would like to find the different components - maybe it is better to say frequencies which constitute the overall time series. These frequencies will not occur in sequence (one after the other) but are combined / in parallel.
Similar to the proposed idea [here](DOI: 10.3233/AIC-2010-0485), I think forecasting the different components separately may improve the overall quality of the prediction.
Think about a smart meter. There one could break up the singular, aggregated time series of the meter into one for the washing machine, one for the fridge, etc., and one more for the remaining noise in order to model and forecast the energy usage separately and improve prediction (cf., nonintrusive load monitoring)?
A similar but different scenario is the history of transactions in a bank account: Here breaking up the cashflow means finding the individual time series of payments (salary, rent, newspapers, etc.) out of the list of discrete transactions. As mentioned, it might be better to talk about seasonality in this case as recurring payments are different seasonal components.
In a more general sense, I would like to find the basic frequencies / periodicities in the data (cf., spectral density estimation) which constitute the overall main time series.