Abstract
In uncertain multivariate time series, the most basic model is the uncertain vector autoregressive (UVAR) model. In this paper, we will propose another uncertain multivariate time series model, the uncertain vector moving average (UVMA) model as well as transform the UVMA model into a UVAR model and use the Welsch loss function to estimate the unknown parameters. Analyzing residuals and forecasting future trends. In addition, the best-fit model is determined based on the value of the sum of squared errors. Finally, the robustness of the Welsch estimation and the validity of the UVMA model are illustrated by numerical examples, and our method is also applicable to predict the data of PM2.5 and PM10.
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