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Forecasting Time Series from Clusters

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  • Marahaj, E.A.
  • Inder, B.

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.

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File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/1999/wp9-99.pdf
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Bibliographic Info

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 9/99.

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Length: 26 pages
Date of creation: Jun 1999
Date of revision:
Handle: RePEc:msh:ebswps:1999-9

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Related research

Keywords: Autoregressive models; Clustering technique; Mean square forecast error; Pooled series;

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  1. Shah, Chandra, 1997. "Model selection in univariate time series forecasting using discriminant analysis," International Journal of Forecasting, Elsevier, vol. 13(4), pages 489-500, December.
  2. Maharaj, E.A., 1994. "A Significance Test for Classifying ARMA Models," Monash Econometrics and Business Statistics Working Papers 18/94, Monash University, Department of Econometrics and Business Statistics.
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