Comparing for Different Time Series Methods the Value of Technical Expertise Individualized Analysis, and Judgmental Adjustment
Technical expertise, human judgment, and the time spent by an analyst are often believed to be key factors in determining the accuracy of forecasts obtained with the use of a time series forecasting method. A control experiment was designed to empirically test these beliefs. It involved the participation of experts and persons with limited training. Forecasts were generated for 25 time series with the use of the Box-Jenkins, Holt-Winters and Carbone-Longini filtering methods. Results of the nonparametric tests used to compare the forecasts confirmed that technical expertise, judgmental adjustment, and individualized analyses were of little value in improving forecast accuracy as compared to black box approaches. In addition, simpler methods were found to provide significantly more accurate forecasts than the Box-Jenkins method when applied by persons with limited training.
Volume (Year): 29 (1983)
Issue (Month): 5 (May)
|Contact details of provider:|| Postal: |
Web page: http://www.informs.org/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:29:y:1983:i:5:p:559-566. 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: (Mirko Janc)
If references are entirely missing, you can add them using this form.