Uncertain regression model with moving average time series errors

Abstract

As a basic model, an uncertain regression model with autoregressive time series errors has been investigated. This paper proposes another fundamental model—uncertain regression model with moving average time series errors—by assuming that the errors of regression model have a moving average structure. Then the principle of least squares is used to estimate the unknown parameters in the model. Based on the fitted model, the forecast value and confidence interval of the future data are derived. Finally, an example is presented to verify the feasibility of this approach.