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Journal Article
The Combining Of Forecasts Using Recursive Techniques With
1989
Overview
Six optimal and 4 ad hoc recursive combination methods are evaluated over 5 actual data sets. The optimal methods are: 1. recursive least squares, 2. Kalman filter, 3. normal discount Bayesian Model, 4. Snyder's model, 5. Snyder's model with maximum likelihood estimate, and 6. alternate estimation of alpha. The ad hoc methods are: 1. the bias-adjusted mean, 2. the bias-adjusted ranks, 3. the recursive bias-adjusted mean, and 4. the recursive bias-adjusted ranks. All of the methods are compared to the mean and recursive least squares methods. The recursive methods are found to be very effective from start-up on 2 of the data sets. Where the optimal methods work well, so do the ad hoc ones, suggesting that often combination methods permitting local bias adjustment may be preferable to the mean forecast and comparable to the optimal methods.
Publisher
Wiley Periodicals Inc
Subject
