Dynamic structural models with covariates for short-term forecasting of time series with complex seasonal patterns

• Type: Another Publication in an International Scientific Journal
• Date: 2021-04

Authors

Puindi, AC (Author) Other; Maria Eduarda Silva (Author) FEP

Journal

Title: Journal of Applied Statistics

Vol. 48 No. 5

Pages: 804-826

ISSN: 0266-4763

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-00R-ZGG

DOI: 10.1080/02664763.2020.1748178

Abstract (EN): This work presents a framework of dynamic structural models with covariates for short-term forecasting of time series with complex seasonal patterns. The framework is based on the multiple sources of randomness formulation. A noise model is formulated to allow the incorporation of randomness into the seasonal component and to propagate this same randomness in the coefficients of the variant trigonometric terms over time. A unique, recursive and systematic computational procedure based on the maximum likelihood estimation under the hypothesis of Gaussian errors is introduced. The referred procedure combines the Kalman filter with recursive adjustment of the covariance matrices and the selection method of harmonics number in the trigonometric terms. A key feature of this method is that it allows estimating not only the states of the system but also allows obtaining the standard errors of the estimated parameters and the prediction intervals. In addition, this work also presents a non-parametric bootstrap approach to improve the forecasting method based on Kalman filter recursions. The proposed framework is empirically explored with two real time series. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Language: English

Type (Professor's evaluation): Scientific