Improving The Quality of Time Series Modeling and Forecasting Using Robust Multivariate Singular Spectrum Analysis

In time series analysis, ignoring outliers leads to misidentification of the model, biased estimation of parameters, and poor predictions. One of the reliable non-parametric methods in predicting and improving the quality of multivariate time series modeling is the multivariate Singular Spectrum Analysis (MSSA) technique, which does not require any initial assumptions. The presence of outliers affects the Frobenius norm of matrix and reduces the efficiency of the MSSA method. In this research, a new version of MSSA based on the L1- norm is proposed. Then the performance of this method is compared with basic MSSA using simulation studies and real data.