Shallot Price Forecasting Models: Comparison among Various Techniques

• Autorzy: Kasemset Chompoonoot , Phuruan Kanokrot , Opassuwan Takron

Identyfikatory

DOI

10.30657/pea.2023.29.40

• Języki publikacji: EN

Abstrakty

EN

Shallot is one of several horticultural products exported from Thailand to various countries. Despite an increase in shallot prices over the years, farmers face challenges in price forecasting due to fluctuations and other relevant factors. While different forecasting techniques exist in the literature, there is no universal approach due to varying problems and datasets. This study focuses on predicting shallot prices in Northern Thailand from January 2014 to December 2020. Traditional and machine learning models, including ARIMA, Holt-Winters, LSTM, and ARIMA-LSTM hybrids, are proposed. The LSTM model considers temperature and rainfall as influencing factors. Evaluation metrics include RMSE, MAE, and MAPE. Results indicate that the ARIMA-LSTM hybrid model performs best, with RMSE, MAE, and MAPE values of 10.275 Baht, 8.512 Baht, and 13.618%, respectively. Implementing this hybrid model can provide shallot farmers with advanced price information for informed decision-making regarding cultivation expansion and production management.

Słowa kluczowe

EN

forecasting; ARIMA method; LSTM networks; Holt-Winters method; value chain

PL

zarządzanie produkcją; prognozowanie; łańcuchy dostaw; łańcuchy dostaw żywności

• Wydawca: Oficyna Wydawnicza Stowarzyszenia Menedżerów Jakości i Produkcji
• Czasopismo: Production Engineering Archives
• Rocznik: 2023
• Tom: Vol. 29, Iss. 4
• Strony: 348--355
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Kasemset Chompoonoot

• Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, 239 Su Thep, Mueang, Chiang Mai, 50200, Thailand

autor

Phuruan Kanokrot

• Graduate Program in Data Science, Chiang Mai University, 239 Su Thep, Mueang, Chiang Mai, 50200, Thailand

autor

Opassuwan Takron

• Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, 239 Su Thep, Mueang, Chiang Mai, 50200, Thailand

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).