Generalising about univariate forecasting methods: further empirical evidence.
Author: Fildes, R. ; Fildes, R. ; Hibon, Michèle ; Makridakis, Spyros ; Meade, N.INSEAD Area: Technology and Operations Management Series: Working Paper ; 96/70/TM Publisher: Fontainebleau : INSEAD, 1996.Language: EnglishDescription: 31 p.Type of document: INSEAD Working Paper Online Access: Click here Abstract: This paper extends the empirical evidence on the forecasting accuracy of extrapolative methods. The robustness of the major conclusions of the M-competition data is examined in the context of the telecommunications data of Fildes (1992). The performance of robust trend, found to be a successful method for forecasting the telecommunications data by Fildes, is compared with that of other successful methods using M-competition data. ALthough it is established that the structure of the telecommunications data is more homogeneous than that of the M-competition data, the major conclusions of the M-competition continue to hold for this new data set. In addition, while the robust trend method is confirmed to be the best performing method for the telecommunications data, for the 1001 M-competition series, this method is outperformed by methods such as single or damped smoothing. However, the performance of smoothing methods is shown to depend on how the smoothing parameters are estimated| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
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Digital Library | Available | BC001175 |
This paper extends the empirical evidence on the forecasting accuracy of extrapolative methods. The robustness of the major conclusions of the M-competition data is examined in the context of the telecommunications data of Fildes (1992). The performance of robust trend, found to be a successful method for forecasting the telecommunications data by Fildes, is compared with that of other successful methods using M-competition data. ALthough it is established that the structure of the telecommunications data is more homogeneous than that of the M-competition data, the major conclusions of the M-competition continue to hold for this new data set. In addition, while the robust trend method is confirmed to be the best performing method for the telecommunications data, for the 1001 M-competition series, this method is outperformed by methods such as single or damped smoothing. However, the performance of smoothing methods is shown to depend on how the smoothing parameters are estimated
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