Prediction‐based adaptive compositional model for seasonal time series analysis

In this paper we propose a new class of seasonal time series models, based on a stable seasonal composition assumption. With the objective of forecasting the sum of the next ℓ observations, the concept of rolling season is adopted and a structure of rolling conditional distributions is formulated. T...

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Bibliographic Details
Published in:Journal of forecasting 2017-11, Vol.36 (7), p.842-853
Main Authors: Chang, Kun, Chen, Rong, Fomby, Thomas B.
Format: Article
Language:English
Subjects:
adaptive forecasting
Forecasting
Forecasting techniques
prediction
Seasonal variations
stable seasonal pattern
Time series
Citations: Items that this one cites
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Summary:In this paper we propose a new class of seasonal time series models, based on a stable seasonal composition assumption. With the objective of forecasting the sum of the next ℓ observations, the concept of rolling season is adopted and a structure of rolling conditional distributions is formulated. The probabilistic properties, estimation and prediction procedures, and the forecasting performance of the model are studied and demonstrated with simulations and real examples.
ISSN:0277-6693
1099-131X
DOI:10.1002/for.2474