Forecasting of jabodetabek train passengers using singular spectrum analysis and holt-winters methods

Abstract

Train is one of the convenient transportation, it also reduce traffic jam in Jabodetabek. In order to maintain the convenience of passengers using train services, it is necessary to predict the number of train passengers as a consideration in the planning of Jabodetabek train services. In this study the Singular Spectrum Analysis (SSA) and Holt-Winters methods are used to predict the number of Jabodetabek train passengers. SSA is a powerful technique for nonparametric time series analysis and forecasting, which decomposes the original time series into the sum of a small number of independent and interpretable components such as a slowly varying trend, oscillatory components and noise. Holt-Winters method are suitable methods for data that contains trends and seasonality. The Holt-Winters method is based on three smoothing equations, one for level, one for trend, and one for seasonality. SSA method in forecasting the number of Jabodetabek train passengers give lower Mean Absolute Percentage Error (MAPE) value than Holt-Winters method. This means SSA method is better than Holt-Winters method in forecasting the number of Jabodetabek train passengers.