IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Decomposition by Causal Forces: A Procedure for Forecasting Complex Time Series

  • J. S. Armstrong

    (The Wharton School)

Causal forces are a way of summarizing forecasters expectations about what will happen to a time series in the future. Contrary to the common assumption for extrapolation, time series are not always subject to consistent forces that point in the same direction. Some are affected by conflicting causal forces; we refer to these as complex times series. It would seem that forecasting these times series would be easier if one could decompose the series to eliminate the effects of the conflicts. Given forecasts subject to high uncertainty, we hypothesized that a time series could be effectively decomposed under two conditions: 1) if domain knowledge can be used to structure the problem so that causal forces are consistent for two or more component series, and 2) when it is possible to obtain relatively accurate forecasts for each component. Forecast accuracy for the components can be assessed by testing how well they can be forecast on early hold-out data. When such data are not available, historical variability may be an adequate substitute. We tested decomposition by causal forces on 12 complex annual time series for automobile accidents, airline accidents, personal computer sales, airline revenues, and cigarette production. The length of these series ranged from 16 years for airline revenues to 56 years for highway safety data. We made forecasts for one to ten horizons, obtaining 800 forecasts through successive updating. For nine series in which the conditions were completely or partially met, the forecast error (MdAPE) was reduced by more than half. For three series in which the conditions were not met, decomposition by causal forces had little effect on accuracy.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://econwpa.repec.org/eps/get/papers/0502/0502015.pdf
Download Restriction: no

Paper provided by EconWPA in its series General Economics and Teaching with number 0502015.

as
in new window

Length: 21 pages
Date of creation: 04 Feb 2005
Date of revision:
Handle: RePEc:wpa:wuwpgt:0502015
Note: Type of Document - pdf; pages: 21
Contact details of provider: Web page: http://econwpa.repec.org

References listed on IDEAS
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.:

as in new window
  1. JS Armstrong & Fred Collopy, 2004. "Causal Forces: Structuring Knowledge for Time-series Extrapolation," General Economics and Teaching 0412003, EconWPA.
  2. Ernst R. Berndt & Neal J. Rappaport, 2001. "Price and Quality of Desktop and Mobile Personal Computers: A Quarter-Century Historical Overview," American Economic Review, American Economic Association, vol. 91(2), pages 268-273, May.
  3. Armstrong, J Scott & Collopy, Fred, 2001. "Identification of Asymmetric Prediction Intervals through Causal Forces," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(4), pages 273-83, July.
  4. Fred Collopy & J. Scott Armstrong, 1992. "Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations," Management Science, INFORMS, vol. 38(10), pages 1394-1414, October.
  5. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
  6. JS Armstrong & Fred Collopy, 2004. "Integration of Statistical Methods and Judgment for Time Series," General Economics and Teaching 0412024, EconWPA.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpgt:0502015. 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: (EconWPA)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.