Article ID: | iaor19971700 |
Country: | United Kingdom |
Volume: | 15 |
Issue: | 5 |
Start Page Number: | 355 |
End Page Number: | 367 |
Publication Date: | Sep 1996 |
Journal: | International Journal of Forecasting |
Authors: | Young Martin R. |
Keywords: | Bayesian modelling |
Akaike’s BAYSEA approach to seasonal decomposition is designed to capture the respective merits of several pre-existing adjustment techniques. BAYSEA is computationally efficient, requires only weak assumptions about the data-generating process, and is based on solid inferential (namely, Bayesian) foundations. This paper presents a model similar to that used in BAYSEA, but based on a double exponential rather than a Gaussian error model. The resulting procedure has the advantages of Akaike’s method, but in addition is resistant to outliers. The optimal decomposition is obtained rapidly using a sparse linear programming code. Confidence bands and predictive intervals can be obtained using Gibbs sampling.