Application and Evaluation of Multivariate Singular Spectrum Analysis in Meteorological Data

Abstract Multivariate Singular Spectrum Analysis (MSSA) is a forecasting method suitable for data that has intricate patterns such as seasonal variations and nonlinear trends. This article aims to shed light and detail insights about how MSSA can be employed in one such use-case of temperature-wind data processing & evaluation. Using MSSA, the data can be decomposed into constituents that lead to an in-depth analysis of understanding the essence and quality assurance of dataset. The research has shown the proven utility of MSSA for temperature and wind prediction. The results provide a method for the analysis and interpretation of complex meteorological data to support decision making in other fields related with meteorology or climate change research.