Forecasting Time Series from Clusters
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.
|Date of creation:||Jun 1999|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: +61 3 99052489
Fax: +61 3 99055474
Web page: http://business.monash.edu/econometrics-and-business-statistics
More information through EDIRC
|Order Information:|| Web: http://business.monash.edu/econometrics-and-business-statistics Email: |
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:msh:ebswps:1999-9. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dr Xibin Zhang)
If references are entirely missing, you can add them using this form.