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Forecasting Time Series from Clusters

Author

Listed:
  • 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.

Suggested Citation

  • Marahaj, E.A. & Inder, B., 1999. "Forecasting Time Series from Clusters," Monash Econometrics and Business Statistics Working Papers 9/99, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:1999-9
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/1999/wp9-99.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
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    Cited by:

    1. Mehmet BARAN & Sýtký SÖNMEZER & Abdülvahid UÇAR, 2015. "Estimating Financial Trends by Cubic B-Spline Fitting via Fisher Algorithm," Turkish Economic Review, KSP Journals, vol. 2(1), pages 20-25, March.

    More about this item

    Keywords

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

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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