Thailand’s Maize Prices Forecasting using Ensemble Technique
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Abstract
Crop prices forecasting is crucial for farmers, policymakers, and investors. This paper aims to propose suitable machine learning models for forecasting Thailand’s maize prices by implementing and comparing various machine learning algorithms, including regression trees (RT), support vector regression (SVR), ensemble bagging with RT and SVR as the base learner (Bag-RT and Bag-SVR), and random forest (RF). The dataset used in this study is collected from two main sources: the Office of Agricultural Economics in Thailand (OAE) and the investing.com website for the period from January 2002 to August 2023 consist of 260 records and 53 features. Given the dataset numerous independent variables, we applied the recursive feature elimination combined with Pearson correlation feature selection method to reduce feature dimensions by focusing on the most relevant features. The prediction models were trained using 10-folds cross validation and evaluated using three metrics: R-squared (R2), mean absolute error (MAE), and root mean square error (RMSE). The top-performing model, Bag-SVR, achieved the best R2 value of 0.961, MAE of 0.234, and RMSE of 0.315 follow by SVR model with R2 value of 0.959, MAE of 0.251, and RMSE of 0.333. In contrast, the RT model demonstrated the lowest performance scores with R2 value of 0.846, MAE of 0.44, and RMSE of 0.617. In conclusion, our study emphasizes the influence of feature selection on model performance and showcases the potential of machine learning models for accurate maize prices forecasting in Thailand.
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References
Sjah, T.; Zainuri, Z. Agricultural supply chain and food security. In Zero Hunger, Springer, 2020; pp 79-88. https://doi.org/10.1007/978-3-319-95675-6_82
Warr, P.; Suphannachart, W. Agricultural productivity growth and poverty reduction: Evidence from Thailand. Journal of Agricultural Economics 2021, 72 (2), 525-546. https://doi.org/10.1111/1477-9552.12412
Jaipong, P.; Sriboonruang, P.; Siripipattanakul, S.; Sitthipon, T.; Kaewpuang, P.; Auttawechasakoon, P. A review of intentions to use artificial intelligence in Big Data Analytics for Thailand agriculture. Review of Advanced Multidisciplinary Science, Engineering & Innovation 2022, 1 (2), 1-8.
Basso, B.; Liu, L. Seasonal crop yield forecast: Methods, applications, and accuracies. advances in agronomy 2019, 154, 201-255. https://doi.org/10.1016/bs.agron.2018.11.002
Shahhosseini, M.; Hu, G.; Pham, H. Optimizing ensemble weights and hyperparameters of machine learning models for regression problems. Machine Learning with Applications 2022, 7, 100251. https://doi.org/10.1016/j.mlwa.2022.100251
Kantanantha, N.; Serban, N.; Griffin, P. Yield and Price Forecasting for Stochastic Crop Decision Planning. Journal of Agricultural, Biological, and Environmental Statistics 2010, 15 (3), 362-380. DOI: 10.1007/s13253-010-0025-7. https://doi.org/10.1007/s13253-010-0025-7
Wang, L.; Duan, W.; Qu, D.; Wang, S. What matters for global food price volatility? Empirical Economics 2018, 54, 1549-1572. https://doi.org/10.1007/s00181-017-1311-9
Ge, Y.; Wu, H. Prediction of corn price fluctuation based on multiple linear regression analysis model under big data. Neural Computing and Applications 2020, 32, 16843-16855. https://doi.org/10.1007/s00521-018-03970-4
Ya, Z.; Pei, K. Factors influencing agricultural products trade between China and Africa. Sustainability 2022, 14 (9), 5589. https://doi.org/10.3390/su14095589
Jain, A.; Marvaniya, S.; Godbole, S.; Munigala, V. A framework for crop price forecasting in emerging economies by analyzing the quality of time-series data. arXiv preprint arXiv:2009.04171 2020.
Mariammal, G.; Suruliandi, A.; Raja, S.; Poongothai, E. Prediction of land suitability for crop cultivation based on soil and environmental characteristics using modified recursive feature elimination technique with various classifiers. IEEE Transactions on Computational Social Systems 2021, 8 (5), 1132-1142. https://doi.org/10.1109/TCSS.2021.3074534
Suruliandi, A.; Mariammal, G.; Raja, S. Crop prediction based on soil and environmental characteristics using feature selection techniques. Mathematical and Computer Modelling of Dynamical Systems 2021, 27 (1), 117-140. https://doi.org/10.1080/13873954.2021.1882505
Shahhosseini, M.; Hu, G.; Archontoulis, S. V. Forecasting corn yield with machine learning ensembles. Frontiers in Plant Science 2020, 11, 1120. https://doi.org/10.3389/fpls.2020.01120
Ribeiro, M. H. D. M.; Ribeiro, V. H. A.; Reynoso-Meza, G.; dos Santos Coelho, L. Multi-objective ensemble model for short-term price forecasting in corn price time series. In 2019 International Joint Conference on Neural Networks (IJCNN), 2019; IEEE: pp 1-8. https://doi.org/10.1109/IJCNN.2019.8851880
Nikou, M.; Mansourfar, G.; Bagherzadeh, J. Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management 2019, 26 (4), 164-174. https://doi.org/10.1002/isaf.1459
Khwaja, A. S.; Anpalagan, A.; Naeem, M.; Venkatesh, B. Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting. Electric Power Systems Research 2020, 179, 106080. https://doi.org/10.1016/j.epsr.2019.106080
Li, Y.; Yu, R.; Shahabi, C.; Liu, Y. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 2017.
Paudel, D.; Boogaard, H.; de Wit, A.; Janssen, S.; Osinga, S.; Pylianidis, C.; Athanasiadis, I. N. Machine learning for large-scale crop yield forecasting. Agricultural Systems 2021, 187, 103016. https://doi.org/10.1016/j.agsy.2020.103016
Li, J.; Chen, W. Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models. International Journal of Forecasting 2014, 30 (4), 996-1015. https://doi.org/10.1016/j.ijforecast.2014.03.016
Jantankaew, P.; Soonthornphisaj, N. Data Analytics for Maize Price Prediction using Regression Algorithms. KKU Research Journal (Graduate Studies) 2023, 23 (2), 92-106.
Roberts, S.; Osborne, M.; Ebden, M.; Reece, S.; Gibson, N.; Aigrain, S. Gaussian processes for time-series modelling. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2013, 371 (1984), 20110550. https://doi.org/10.1098/rsta.2011.0550
Kim, K.-j. Financial time series forecasting using support vector machines. Neurocomputing 2003, 55 (1-2), 307-319. https://doi.org/10.1016/S0925-2312(03)00372-2
Ouyang, H.; Wei, X.; Wu, Q. Agricultural commodity futures prices prediction via long-and short-term time series network. Journal of Applied Economics 2019, 22 (1), 468-483. https://doi.org/10.1080/15140326.2019.1668664
Cerqueira, V.; Torgo, L.; Pinto, F.; Soares, C. Arbitrated ensemble for time series forecasting. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part II 10, 2017; Springer: pp 478-494. https://doi.org/10.1007/978-3-319-71246-8_29
Oliveira, M.; Torgo, L. Ensembles for time series forecasting. In Asian Conference on Machine Learning, 2015; PMLR: pp 360-370.
Silva, R. F.; Barreira, B. L.; Cugnasca, C. E. Prediction of Corn and Sugar Prices Using Machine Learning, Econometrics, and Ensemble Models. Engineering Proceedings 2021, 9 (1), 31. https://doi.org/10.3390/engproc2021009031
Breiman, L. Classification and regression trees; Routledge, 2017. https://doi.org/10.1201/9781315139470
Chen, R.; Liang, C.-Y.; Hong, W.-C.; Gu, D.-X. Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Applied Soft Computing 2015, 26, 435-443. https://doi.org/10.1016/j.asoc.2014.10.022
Cortes, C.; Vapnik, V. Support-vector networks. Machine learning 1995, 20, 273-297. https://doi.org/10.1007/BF00994018
Dietterich, T. G. Ensemble methods in machine learning. In International workshop on multiple classifier systems, 2000; Springer: pp 1-15.
Breiman, L. Random forests. Machine learning 2001, 45, 5-32. https://doi.org/10.1007/3-540-45014-9_1