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Prediction‐based adaptive compositional model for seasonal time series analysis
Author(s) -
Chang Kun,
Chen Rong,
Fomby Thomas B.
Publication year - 2017
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2474
Subject(s) - series (stratigraphy) , probabilistic logic , computer science , time series , econometrics , seasonality , probabilistic forecasting , class (philosophy) , mathematics , machine learning , artificial intelligence , geology , paleontology
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.