Structural Models: A Computational Look for Signal Extraction and Forecasting Seasonal Time Series

Abstract

Data analysis requires doing statistical programming which can be done at a simpler or more complex level. This article presents the main computational aspects for signal extraction, estimation and forecasting seasonal time series. From a structural model with covariates and different errors affecting the observations and the states, intelligent computational procedures are designed. First, the intelligent computational algorithms to obtain the main matrices of the system are derived; second, a general intelligent computational procedure and an algorithm that performs the Kalman filter and model estimation are also derived. Furthermore, the intelligent computational procedures and the structural model are evaluated using real seasonal time series, and the results demonstrate that the proposed method and model are very attractive and promising for model estimation, signal extraction and forecasting tasks.