Prediction-based adaptive compositional model for seasonal time series analysis

Abstract

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.