Optimization of Triple Exponential Smoothing Parameters Using GSSMV Algorithm and Genetic Algorithm for Determining the Best Forecasting Optimization Model and Predicting the Number of Train Passengers
Abstract
This study compares two optimized Triple Exponential Smoothing (TES) forecasting models for predicting the number of Indonesian train passengers on Java: TES optimized with the GSSMV algorithm (TES-GSSMV) and TES optimized with a genetic algorithm (TES-Genetics). Using 124 time-series data points, both models estimate TES parameters (alpha, beta, gamma) and are evaluated using MAPE. Results show TES-Genetics achieves better prediction accuracy than TES-GSSMV, with a MAPE improvement of 1.393%, so TES-Genetics is selected as the best forecasting optimization model and its alpha, beta, gamma parameters are used for final passenger predictions.
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