Indonesia's Food Commodity Price Forecasting using Recurrent Neural Networks

Savira Amalia, Arian Dhini, Zulkarnain

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Managing strategic commodities prices in the market is considered an important task since they have a significant contribution to the calculation of the inflation rate. Inflation management has a strong connection to the public's economic activities and buying power. To aid this problem, it is necessary to find the best forecasting model that can predict commodities daily price. This paper aims to find the best prediction model between Recurrent Neural Network (RNN) variants, LSTM and GRU, in forecasting the daily price of three Indonesia's strategies commodities: rice, broiler meat, and chicken egg. The result shows that the GRU model achieves higher accuracy in predicting the daily price of rice, broiler meat, and chicken egg, based on two evaluation metrics Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The GRU model also managed to finish the computational process faster than LSTM by$\sim$20 seconds.

Original languageEnglish
Title of host publicationProceedings of International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665468831
DOIs
Publication statusPublished - 2022
Event2022 International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022 - Kochi, India
Duration: 23 Jun 202225 Jun 2022

Publication series

NameProceedings of International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022

Conference

Conference2022 International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022
Country/TerritoryIndia
CityKochi
Period23/06/2225/06/22

Keywords

  • Forecasting
  • GRU
  • LSTM
  • strategic commodity

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