Application of Multivariate Singular Spectrum Analysis for Weather Prediction - Abdul Mukti; Kartika Maulida Hindrayani; Mohammad Idhom (Komunitas Dosen Indonesia, 2025)
Weather significantly influences various aspects of life, especially in urban areas like Surabaya, where unpredictable weather can disrupt transportation, public health, economic activities, and overall comfort. Among the key meteorological variables, air temperature and relative humidity are crucial for assessing human thermal comfort, as their interaction forms the heat index a key indicator of health risks in tropical regions. This study introduces the use of the Multivariate Singular Spectrum Analysis (MSSA) method to forecast daily weather parameters, including minimum temperature (TN), maximum temperature (TX), average temperature (TAVG), and average relative humidity (RH_AVG). The research utilized weather data from the Perak 1 Meteorological Station in Surabaya, spanning from August 1 to December 31, 2024 (training data) and January 1 to January 14, 2025 (testing data). Unlike traditional methods, the MSSA model effectively analyzes the complex relationships between multiple weather variables, improving forecasting accuracy. The model demonstrated strong performance, with Mean Absolute Percentage Errors (MAPE) of 3.70% for TN, 5.99% for TX, 4.44% for TAVG, and 7.39% for RH_AVG. These results highlight MSSA's potential as an effective tool for short-term weather forecasting in urban tropical environments, supporting more accurate predictions that can inform early warning systems, disaster planning, and public health strategies. This work advances the state-of-the-art by offering a robust method for handling multivariate weather data, which is essential for making informed decisions in rapidly changing climates