7
$\begingroup$

I have finance data that I need to forecast out for 7 years. My data is generally debits and credits, and those are split into a number of sub-series which share common traits (e.g. similar seasonality and/or trend).

My question is, is it appropriate to

  1. split the total series into component parts;
  2. forecast each part in the most appropriate manner;
  3. re-aggregate the parts into a complete projection?

For example, I'd like to treat the following sub-series/parts differently:

Expenditures

  • Wages/Pension (annual percent increase, based upon contracts)
  • Insurance (annual percent increase based upon prior increases)
  • Supplies (annual percent increase based upon different prior increases)

Then, add the individual sub-series back into a single forecast. Obviously, there would be similar but different sub-series/parts for revenue, which would eventually be combined for a total forecast.

Improve this question
$\endgroup$
2
  • 2
    $\begingroup$ The short answer is yes. The tricky part is getting prediction intervals on the re-aggregated forecasts as your components are probably correlated. $\endgroup$
    – Rob Hyndman
    Commented Aug 9, 2012 at 0:06
  • $\begingroup$ Did you look at Singular Spectrum Analysis? (R: cran.r-project.org/web/packages/Rssa/index.html). It can be used for decomposition and forecasting of time series. $\endgroup$
    – LE Rogerson
    Commented Nov 7, 2016 at 13:31

0

Reset to default

Your Answer

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

Browse other questions tagged or ask your own question.