Hybrid SSA-TBATS to improve forecasting accuracy on export value data in Indonesia

Setiawan Setiawan*, Muhammad Fajar, Hasbi Yasin, Chrisandi R. Lande

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This research aims to present the Hybrid SSA-TBATS approach as an alternate forecasting technique that does not need specific assumptions or requirements such as stationarity, linear or non-linear process, and normality. This analysis used Indonesian exports (in millions of USD) from January 1993 to July 2022. The findings of this research reveal that the Hybrid SSA-TBATS method outperforms SSA and TBATS in forecasting accuracy and defines the window length and number of groups. Therefore, it is highly recommended based on MAPE since it does not need any information on the characteristics of the data to be forecasted.

Original languageEnglish
Pages (from-to)1505-1514
Number of pages10
JournalInternational Journal of Data and Network Science
Volume7
Issue number4
DOIs
Publication statusPublished - 1 Sept 2023

Keywords

  • Export
  • Forecasting
  • Singular Spectrum Analysis
  • TBATS
  • Time Series

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