Uncertain threshold autoregressive model with imprecise observations

Abstract

Uncertain time series analysis is a methodology that deals with expert’s experimental time series data. Previous studies mainly focus on linear models such as an uncertain autoregressive (UAR) model. Nevertheless, the laws of motion in the real world are usually non linear. In order to model the observations that periodically vary over time, this article introduces an uncertain threshold autoregressive (UTAR) model. Then unknown parameters in the UTAR model can be estimated with the least squares estimation and residual analysis is presented. Furthermore, we discuss the forecast value and confidence interval for variables in the next periods. Ultimately, a numerical example is given.

Topics & Concepts