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A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh

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  • Mst Noorunnahar
  • Arman Hossain Chowdhury
  • Farhana Arefeen Mila

Abstract

In this study, we attempt to anticipate annual rice production in Bangladesh (1961–2020) using both the Autoregressive Integrated Moving Average (ARIMA) and the eXtreme Gradient Boosting (XGBoost) methods and compare their respective performances. On the basis of the lowest Corrected Akaike Information Criteria (AICc) values, a significant ARIMA (0, 1, 1) model with drift was chosen based on the findings. The drift parameter value shows that the production of rice positively trends upward. Thus, the ARIMA (0, 1, 1) model with drift was found to be significant. On the other hand, the XGBoost model for time series data was developed by changing the tunning parameters frequently with the greatest result. The four prominent error measures, such as mean absolute error (MAE), mean percentage error (MPE), root mean square error (RMSE), and mean absolute percentage error (MAPE), were used to assess the predictive performance of each model. We found that the error measures of the XGBoost model in the test set were comparatively lower than those of the ARIMA model. Comparatively, the MAPE value of the test set of the XGBoost model (5.38%) was lower than that of the ARIMA model (7.23%), indicating that XGBoost performs better than ARIMA at predicting the annual rice production in Bangladesh. Hence, the XGBoost model performs better than the ARIMA model in predicting the annual rice production in Bangladesh. Therefore, based on the better performance, the study forecasted the annual rice production for the next 10 years using the XGBoost model. According to our predictions, the annual rice production in Bangladesh will vary from 57,850,318 tons in 2021 to 82,256,944 tons in 2030. The forecast indicated that the amount of rice produced annually in Bangladesh will increase in the years to come.

Suggested Citation

  • Mst Noorunnahar & Arman Hossain Chowdhury & Farhana Arefeen Mila, 2023. "A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-15, March.
    • Handle: RePEc:plo:pone00:0283452
    DOI: 10.1371/journal.pone.0283452
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    References listed on IDEAS

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    1. Rahman, Mohammad Chhiddikur & Islam, Mohammad Ariful & Rahaman, Md Shajedur & Sarkar, Md Abdur Rouf & Ahmed, Rokib & Kabir, Md Shahjahan, 2021. "Identifying the Threshold Level of Flooding for Rice Production in Bangladesh: An Empirical Analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(2), pages 243-250.
    2. Md Siddikur Rahman & Arman Hossain Chowdhury, 2022. "A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-14, September.
    3. Md Siddikur Rahman & Arman Hossain Chowdhury & Miftahuzzannat Amrin, 2022. "Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh," PLOS Global Public Health, Public Library of Science, vol. 2(5), pages 1-13, May.
    4. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
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    1. Jihong Sun & Chen Sun & Zhaowen Li & Ye Qian & Tong Li, 2024. "Prediction method of sugarcane important phenotype data based on multi-model and multi-task," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-25, December.

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