Multivariate Time Series for Customs Revenue Forecasting Using LSTM Neural Networks

Cahyono, Novan Dwi; Sumpeno, Surya; Setiiadi, Eko

doi:10.1109/icitcom60176.2023.10442562

2023 International Conference on Information Technology and Computing (ICITCOM) 2023

DOI: 10.1109/icitcom60176.2023.10442562

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Multivariate Time Series for Customs Revenue Forecasting Using LSTM Neural Networks

Novan Dwi Cahyono,

Surya Sumpeno,

Eko Setiiadi

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2024

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Cited by 2 publications

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Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

Gur

2024

DSFE

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<p>This study aims to apply advanced machine-learning models and hybrid approaches to improve the forecasting accuracy of the US Consumer Price Index (CPI). The study examined the performance of LSTM, MARS, XGBoost, LSTM-MARS, and LSTM-XGBoost models using a large time-series data from January 1974 to October 2023. The data were combined with key economic indicators of the US, and the hyperparameters of the forecasting models were optimized using genetic algorithm and Bayesian optimization methods. According to the VAR model results, variables such as past values of CPI, oil prices (OP), and gross domestic product (GDP) have strong and significant effects on CPI. In particular, the LSTM-XGBoost model provided superior accuracy in CPI forecasts compared with other models and was found to perform the best by establishing strong relationships with variables such as the federal funds rate (FFER) and GDP. These results suggest that hybrid approaches can significantly improve economic forecasts and provide valuable insights for policymakers, investors, and market analysts.</p>

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Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

Gur

2024

DSFE

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show abstract

Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

Gur

2024

DSFE

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Multivariate Time Series for Customs Revenue Forecasting Using LSTM Neural Networks

Cited by 2 publications

References 19 publications

Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

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