Forecasting Time Series from Clusters

E.A. Marahaj and Brett Inder

No 9/99, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Forecasting large numbers of time series is a costly and time-consuming exercise. Before forecasting a large number of series that are logically connected in some way, the authors can first cluster them into groups of similar series. In this paper they investigate forecasting the series in each cluster. Similar series are first grouped together using a clustering procedure that is based on a test of hypothesis. The series in each cluster are then pooled together and forecasts are obtained. Simulated results show that this procedure for forecasting similar series performs reasonably well.

Keywords: Autoregressive models; Clustering technique; Mean square forecast error; Pooled series (search for similar items in EconPapers)
JEL-codes: C22 C32 (search for similar items in EconPapers)
Pages: 26 pages
Date: 1999-06
New Economics Papers: this item is included in nep-ets
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Citations: View citations in EconPapers (3)

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