Abstract

In the age of data, governments can use AI to achieve goals. One of the objectives is to find an appropriate way to accurately forecast international trade tax revenues, especially import duties. This is very important for state budget planning and achieving Key Performance Indicator target. Because if the planning misses, then the customs authority will increase the risk of failure in meeting the target. This research uses multivariate time series problem approach to produce a forecasting model with decomposition and recurrent neural network Long Short-Term Memory (LSTM). Our model employs a supervised mechanism and is trained on historical data with the seasonal component removed, resulting in a robust model that can be applied to various customs administrations. We have evaluated our proposed model on the real-world data. Results show promising performance, the LSTM Decomposition with the lowest RMSE value of 37,720 outperformed both the Seasonal ARIMA with RMSE 61,232 and the Holt-Winters Model with RMSE 47,703. When compared to LSTM without decomposition, our proposed model achieves a 17% improvement and a 38.4% reduction in error when compared to the seasonal ARIMA.

Original languageEnglish
Title of host publicationProceeding - International Conference on Information Technology and Computing 2023, ICITCOM 2023
EditorsHsing-Chung Chen, Cahya Damarjati, Christian Blum, Yessi Jusman, Siti Nurul Aqmariah Mohd Kanafiah, Waleed Ejaz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages357-362
Number of pages6
ISBN (Electronic)9798350359633
DOIs
Publication statusPublished - 2023
Event2023 International Conference on Information Technology and Computing, ICITCOM 2023 - Hybrid, Yogyakarta, Indonesia
Duration: 1 Dec 20232 Dec 2023

Publication series

NameProceeding - International Conference on Information Technology and Computing 2023, ICITCOM 2023

Conference

Conference2023 International Conference on Information Technology and Computing, ICITCOM 2023
Country/TerritoryIndonesia
CityHybrid, Yogyakarta
Period1/12/232/12/23

Keywords

  • customs revenue
  • forecasting
  • lstm
  • multivariate
  • neural network
  • time series

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