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… Click to show full 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.
               
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