Times Series Analysis Without Model Identification

The present paper explores employing a general transformation to avoid the model identification step, which permits the employment of time series analysis in a wider variety of situations as a result of relaxing the requirement of a large number of points for model identification.

Abstract

Time series analysis is a method for analyzing repeated observations on a single unit. Previously developed approaches involve a two stage process: (1) identifying which of various ARIMA (p,d,q) models best describe the underlying process; and (2) on the basis of the identified model, transforming the observed data to meet the assumptions (i.e., independence of data) of the general linear model, and estimating and testing the intervention effects. The present paper explores employing a general transformation to avoid the model identification step. This approach permits the employment of time series analysis in a wider variety of situations as a result of relaxing the requirement of a large number of points for model identification. The generalized transformation approach permits alternative computational procedures, based on a generalized least squares algorithm, that has greater flexibility and efficiency.