International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 17, No. 3, 8 Jun. 2025
Cover page and Table of Contents: PDF (size: 432KB)
Forecasting Agriculture Commodity Price Trend using Novel Competitive Ensemble Regression Model
PDF (432KB), PP.97-105
Views: 0 Downloads: 0
Author(s)
R. Ragunath
1
R. Rathipriya
1,*
Index Terms
Agricultural Commodity, Wholesale Pricing Index, Competitive Ensemble, Ensemble Regression Models, Price Trend Prediction
Abstract
This paper introduces a novel approach for forecasting the price trends of agricultural commodities to address the issue of price volatility faced by both farmers and consumers. The accurate forecasting of food prices is particularly crucial in emerging nations such as India where food security is a top priority. To achieve this goal, the paper presents an ensemble learning-based approach for predicting the agricultural commodity price (ACP) trend. Using dataset namely rainfall and wholesale pricing index (WPI), the study compares the performance of various individual and ensemble regression models. The findings of this work demonstrated that the novel competitive ensemble regression (CER) approach outperforms traditional individual regression models in predicting price fluctuations trend accurately. This approach has the high potential and more precise prediction to afford farmers and dealers, also make the model suitable for the financial industries.
Cite This Paper
R. Ragunath, R. Rathipriya, "Forecasting Agriculture Commodity Price Trend using Novel Competitive Ensemble Regression Model", International Journal of Information Technology and Computer Science(IJITCS), Vol.17, No.3, pp.97-105, 2025. DOI:10.5815/ijitcs.2025.03.07
Reference
[1]D. Yang and Z. Liu, "Does farmer economic organization and agricultural specialization improve rural income? Evidence from China," Economic Modelling, vol. 29, no. 3, pp. 990–993, 2012.
[2]S. Yao, Y. Guo, and X. Huo, "An empirical analysis of the effects of China's land conversion program on farmers' income growth and labor transfer," Journal of Environmental Management, vol. 45, no. 3, pp. 502–512, 2010.
[3]L.-J. Chen, C. Ye, S.-W. Hu, V. Wang, and J. Wen, "The effect of a target zone on the stabilization of agricultural prices and farmers' nominal income," Journal of Agricultural and Resource Economics, vol. 38, no. 1, pp. 34–47, 2013.
[4]P. Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge: Cambridge University Press, 2012.
[5]D. Kumar and S. K. Rath, "Predicting the trends of price for Ethereum using deep learning technique," in Artificial Intelligence and Evolutionary Computations in Engineering Systems, S. Dash, C. Lakshmi, S. Das, and B. Panigrahi, Eds., Singapore: Springer, 2020, pp. 103–114.
[6]G. Milunovich, "Forecasting Australia’s real house price index: A comparison of time series and machine learning methods," Journal of Forecasting, vol. 39, no. 7, pp. 1098–1118, 2020.
[7]P. J. Werbos, "Generalization of backpropagation with application to a recurrent gas market model," Neural Network, vol. 1, no. 4, pp. 339–356, 1998.
[8]G. P. Zhang and M. Qi, "Neural network forecasting for seasonal and trend time series," Eur. J. Oper. Res., vol. 160, pp. 501–514, 2005.
[9]G. P. Zhang and D. M. Kline, "Quarterly time-series forecasting with neural networks," IEEE Trans. Neural Netw., vol. 18, pp. 1800–1814, 2007.
[10]Y. Weng, X. Wang, J. Hua, H. Wang, M. Kang, and F. Y. Wang, "Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by web crawler," IEEE Trans. Comput. Soc. Syst., vol. 6, pp. 547–553, 2019.
[11]J. E. Sheehy, P. L. Mitchell, and A. B. Ferrer, "Decline in rice grain yields with temperature: Models and correlations can give different estimates," Field Crop Res., vol. 98, no. 2–3, pp. 151–156, 2006.
[12]Y. Zhang and S. Na, "A novel agricultural commodity price forecasting model based on fuzzy information granulation and Mea-SVM model," Mathematical Problems in Engineering, vol. 2018, pp. 1–10, 2018.
[13]R. K. Paul, M. Yeasin, P. Kumar, P. Kumar, M. Balasubramanian, H. S. Roy, et al., "Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India," PLoS ONE, vol. 17, no. 7, p. e0270553, 2022.
[14]A. Chatzimparmpas, R. M. Martins, K. Kucher, and A. Kerren, "StackGenVis: Alignment of data, algorithms, and models for stacking ensemble learning using performance metrics," IEEE Trans. Vis. Comput. Graph., vol. 27, pp. 1547–1557, 2021.
[15]Z. Li, Z. Chen, Q. Cheng, F. Duan, R. Sui, X. Huang, and H. Xu, "UAV-based hyperspectral and ensemble machine learning for predicting yield in winter wheat," Agronomy, vol. 12, p. 202, 2022.
[16]S. V. Razavi-Termeh, A. Sadeghi-Niaraki, and S. M. Choi, "Spatial modeling of asthma-prone areas using remote sensing and ensemble machine learning algorithms," Remote Sens., vol. 13, p. 3222, 2021.
[17]M. Ustuner and F. B. Sanli, "Polarimetric target decompositions and light gradient boosting machine for crop classification: A comparative evaluation," ISPRS Int. J. Geo-Inf., vol. 8, 2019.
[18]Y. Zhang, J. Liu, and W. Shen, "A review of ensemble learning algorithms used in remote sensing applications," Applied Sciences, vol. 12, no. 17, p. 8654, 2022.
[19]A. M. Ticlavilca, D. M. Feuz, and M. Mckee, "Forecasting agricultural commodity prices using multivariate Bayesian machine learning regression," NCCC-134, 2010.
[20]L. Madaan, A. Sharma, P. Khandelwal, S. Goel, P. Singla, and A. Seth, "Price forecasting & anomaly detection for agricultural commodities in India," in Proceedings of the Conference on Computing & Sustainable Societies - COMPASS 19, 2019.
[21]Y. Guo, D. Tang, W. Tang, S. Yang, Q. Tang, Y. Feng, and F. Zhang, "Agricultural price prediction based on combined forecasting model under spatial-temporal influencing factors," Sustainability, vol. 14, no. 17, p. 10483, 2022.
[22]Y. H. Gu, D. Jin, H. Yin, R. Zheng, X. Piao, and S. J. Yoo, "Forecasting agricultural commodity prices using dual input attention LSTM," Agriculture, vol. 12, no. 2, p. 256, 2022.
[23]V. Talasila, C. Prasad, G. Reddy, and A. Aparna, "Analysis and prediction of crop production in Andhra region using deep convolutional regression network," Int. J. Intell. Eng. Syst., vol. 13, no. 5, pp. 1–9, 2020.
[24]L. Mao, Y. Huang, X. Zhang, S. Li, and X. Huang, "ARIMA model forecasting analysis of the prices of multiple vegetables under the impact of the COVID-19," PLoS ONE, vol. 17, no. 7, 2022.
[25]A. K. Mahto, M. A. Alam, R. Biswas, J. Ahmed, and S. I. Alam, "Short-term forecasting of agriculture commodities in context of Indian market for sustainable agriculture by using the artificial neural network," Journal of Food Quality, vol. 2021, pp. 1–13, 2021.
[26]X. Wang, S. Gao, Y. Guo, S. Zhou, Y. Duan, and D. Wu, "A combined prediction model for Hog Futures prices based on WOA-LIGHTGBM-CEEMDAN," Complexity, vol. 2022, pp. 1–15, 2022.
[27]R. K. Paul, M. Yeasin, P. Kumar, P. Kumar, M. Balasubramanian, H. S. Roy, et al., "Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India," PLoS ONE, vol. 17, no. 7, 2022.
[28]N. Soni and J. Raut, "Crop price prediction using machine learning techniques," 2022.
[29]D. Zhang, S. Chen, L. Liwen, and Q. Xia, "Forecasting agricultural commodity prices using model selection framework with time series features and forecast horizons," IEEE Access, vol. 8, pp. 28197–28209, 2020.
[30]M. Nabipour, P. Nayyeri, H. Jabani, A. Mosavi, E. Salwana, and S. S., "Deep learning for stock market prediction," Entropy, vol. 22, no. 8, p. 840, 2020.
[31]Z. Chen, H. S. Goh, K. L. Sin, K. Lim, N. K. Chung, and X. Y. Liew, "Automated agriculture commodity price prediction system with machine learning techniques," Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 376–384, 2021.
[32]H. P. Suresha, M. Uppaladinni, and K. K. Tiwari, "Indian commodity market price comparative study of forecasting methods - A case study on onion, potato and tomato," Asian Journal of Research in Computer Science, vol. 12, no. 4, pp. 147–159, 2021.
[33]A. A. Adeniran, A. R. Adebayo, H. O. Salami, M. O. Yahaya, and A. A. Abdulraheem, "A competitive ensemble model for permeability prediction in heterogeneous oil and gas reservoirs," Applied Computing and Geosciences, vol. 1, p. 100004, 2019.
[34]A. H. Utomo, M. A. Gumilang, and A. Ahmad, "Agricultural commodity sales recommendation system for farmers based on geographic information systems and price forecasting using probabilistic neural network algorithm," in IOP Conference Series: Earth and Environmental Science, vol. 980, no. 1, p. 012061, 2022.
[35]S. Bayona-Oré, R. Cerna, and E. T. Hinojoza, "Machine learning for price prediction for agricultural products," WSEAS Transactions on Business and Economics, vol. 18, pp. 969–977, 2021.
[36]M. Shahhosseini, G. Hu, and H. Pham, "Optimizing ensemble weights and hyperparameters of machine learning models for regression problems," Machine Learning with Applications, vol. 7, p. 100251, 2022.
[37]T. van Klompenburg, A. Kassahun, and C. Catal, "Crop yield prediction using machine learning: A systematic literature review," Computers and Electronics in Agriculture, vol. 177, p. 105709, 2020.