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All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
MUHAMMAD RIZAL
UIN Sunan Kalijaga Yogyakarta
Computer Science & IT

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FORECASTING USING SARIMA AND BAYESIAN STRUCTURAL TIME SERIES METHOD FOR RANGE SEASONAL TIME MUHAMMAD RIZAL; Sri Utami Zuliana
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.402

Abstract

Data containing seasonal patterns, the SARIMA and Bayesian Structural Time Series methods, are time series methods that can be used on this type of data. This research aims to determine the steps of the SARIMA model and Bayesian Structural Time Series, applying the SARIMA model and Structural Bayesians Time Series, get the forecasting results of the SARIMA model and Bayesian Structural Time Series with MAPE measurements. The research method used is a quantitative method applied to data on the number of PT KAI train passengers in the Java region for 2006-2019. The results of this research show that the best model for forecasting the number of PT KAI train passengers in the Java region in 2006-2019 is SARIMA (2,1,0)(0,1,2)[12] with a MAPE value of 4.77% compared to the Bayesian method structural time series [12] namely 5.25%.
Co-Authors Zuliana, Sri Utami