Leveraging Partner Country Factors in Deep Learning for Thailand’s Forecasted Inflation Accuracy Enhancement

Authors

  • Techin Arnonwattana Chulalongkorn University
  • Parames Chutima Chulalongkorn University; The Royal Society of Thailand

DOI:

https://doi.org/10.4186/ej.2024.28.6.37

Keywords:

inflation forecasting, neural networks, time series models, hybrid models, partner countries' inflation, forecast accuracy, deep learning, Bank of Thailand, economic indicators

Abstract

This paper focuses on improving the accuracy of headline inflation forecasts in Thailand. By evaluating the performance of deep learning models, time series forecasting models, and hybrid models in 1-, 3-, 6-, and 12-month advance forecast periods are investigated. In addition, the efficacy of including partner countries' inflation variables in the model is evaluated. There is a comparative analysis of various models, including ANN, RNN, LSTM, VAR, the hybrid model (VAR-ANN), and the BOTMM benchmark model of the Bank of Thailand. This study aimed to identify the most efficient model and demonstrate the impact of including partner countries' inflation on forecast accuracy. The results reveal that the hybrid model (VAR-ANN) consistently outperforms other models over several forecast periods, showing its superiority in capturing inflation trends. Specifically, the hybrid model (VAR-ANN) shows an average RMSE improvement of 50.36% over the BOTMM benchmark model from 2020 to 2022, with performance improvements of 52.94% in 2020, 56.56% in 2021, and 47.25% in 2022. In addition, the inclusion of partner countries' inflation significantly increases the accuracy of the predictions. These results are helpful for policymakers and practitioners working on inflation forecasts and emphasize the practical advantages of the hybrid model for enhancing prediction accuracy for Thailand's economic indicators.

Downloads

Download data is not yet available.

Author Biographies

Techin Arnonwattana

Regional Centre for Manufacturing Systems Engineering, Chulalongkorn University, Thailand

Parames Chutima

Regional Centre for Manufacturing Systems Engineering, Chulalongkorn University, Thailand

Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Thailand

The Academy of Science, The Royal Society of Thailand, Thailand

Downloads

Published In
Vol 28 No 6, Jun 30, 2024
How to Cite
[1]
T. Arnonwattana and P. Chutima, “Leveraging Partner Country Factors in Deep Learning for Thailand’s Forecasted Inflation Accuracy Enhancement”, Eng. J., vol. 28, no. 6, pp. 37-58, Jun. 2024.

Most read articles by the same author(s)

1 2 > >>