<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Shallot Price Forecasting Models: Comparison among Various Techniques

Department of Industrial Engineering, Faculty of Engineering

Graduate Program in Data Science, Chiang Mai University

Production Engineering Archives

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Shallot Price Forecasting Models: Comparison among Various Techniques"}

Shallot is one of several horticultural products exported from Thailand to various countries. Despite an increase in shallot prices over the years, farmers...

Shallot Price Forecasting Models: Comparison among Various Techniques

Data publikacji: 28 paź 2023

Zakres stron: 348 - 355

Otrzymano: 13 lis 2022

Przyjęty: 04 lip 2023

DOI: https://doi.org/10.30657/pea.2023.29.40

Słowa kluczowe
Forecasting, ARIMA, LSTM, Holt-Winters, ARIMA-LSTM, Value Chain

© 2023 Chompoonoot Kasemset et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Shallot Price Forecasting Models: Comparison among Various Techniques

Data publikacji: 28 paź 2023

Zakres stron: 348 - 355

Otrzymano: 13 lis 2022

Przyjęty: 04 lip 2023

DOI: https://doi.org/10.30657/pea.2023.29.40

Słowa kluczoweForecasting, ARIMA, LSTM, Holt-Winters, ARIMA-LSTM, Value Chain

© 2023 Chompoonoot Kasemset et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Forecasting, ARIMA, LSTM, Holt-Winters, ARIMA-LSTM, Value Chain