Research Article
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Year 2023, , 422 - 426, 01.07.2023
https://doi.org/10.47115/bsagriculture.1304625

Abstract

References

  • Bayona-Oré S, Cerna R, Tirado Hinojoza E. 2021. Machine learning for price prediction for agricultural products. Wseas Transact Busin Econ, URL: https://doi.org/10.37394/23207.2021.18.92 (accessed date: March 11, 2022).
  • Bengio Y, Courville A, Vincent P. 2013. Representation learning: A review and new perspectives. IEEE Transact Pattern Anal Machine Intell, 35(8): 1798-1828. https://doi.org/10.1109/TPAMI.2013.50.
  • Casado-Vara R, Martin del Rey A, Pérez-Palau D, de-la-Fuente-Valentín L, Corchado J. M. 2021. Web traffic time series forecasting using LSTM neural networks with distributed asynchronous training. Math, 9(4): 421. https://doi.org/10.3390/math9040421.
  • Dharavath R, Khosla E. 2019. Seasonal ARIMA to forecast fruits and vegetable agricultural prices. In Proceedings of the IEEE International Symposium on Smart Electronic Systems, 16-18 December, Rourkela, India, pp: 47-52.
  • Dingli A, Fournier KS. 2017. Financial time series forecasting-a deep learning approach. Inter J Mach Learn Comput, 7(5): 118-122.
  • Duarte AMA, Gaglianone WP, de Carvalho Guillén OT, Issler JV. 2021. Commodity prices and global economic activity: a derived-demand approach. Energy Econ, 96: 105120. https://doi.org/10.1016/j.eneco.2021.105120.
  • Espinosa R, Palma J, Jiménez F, Kamińska J, Sciavicco G, Lucena-Sánchez E. 2021. A time series forecasting based multi-criteria methodology for air quality prediction. Appl Soft Comput, 113: 107850. https://doi.org/10.1016/j.asoc.2021.107850.
  • Freeman BS, Taylor G, Gharabaghi B, Thé J. 2018. Forecasting air quality time series using deep learning. J Air Waste Manage Assoc, 68(8): 866-886. https://doi.org/10.1080/10962247.2018.1459956.
  • Jebb AT, Tay L, Wang W, Huang Q. 2015. Time series analysis for psychological research: examining and forecasting change. Front Psychol, 6: 727.
  • Klompenburg T, Kassahun A, Catal C. 2020. Crop yield prediction using machine learning: A systematic literature review. Comput Electron Agri, 177. 105709. 10.1016/j.compag.2020.105709.
  • Kırbaş İ, Sözen A, Tuncer A. D, Kazancıoğlu F. Ş. 2020. Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA NARNN and LSTM approaches. Chaos Solitons Fractals, 138: 110015. https://doi.org/10.1016/j.chaos.2020.110015.
  • Madaan L, Sharma A, Khandelwal P, Goel S, Singla P, Seth A. 2019. Price forecasting anomaly detection for agricultural commodities in India. In Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies, July 3 – 5, Accra, Ghana, pp: 52-64.
  • Mili S, Bouhaddane M. 2021. Forecasting global developments and challenges in olive oil supply and demand: A Delphi survey from Spain. Agri, 11(3): 191. https://doi.org/10.3390/agriculture11030191.
  • Molitor K, Braun B, Pritchard B. 2017. The effects of food price changes on smallholder production and consumption decision‐making: evidence from Bangladesh. Geograp Res, 55(2): 206-216. https://doi.org/10.1111/1745-5871.12225.
  • Navaratnalingam S, Kodagoda N, Suriyawansa K. 2020. Exploiting multivariate LSTM models with multistep price forecasting for agricultural produce in Sri Lankan context. In Proceedings of 2nd International Conference on Advancements in Computing (ICAC), 10-11 December 2020, Colombo, Sri Lanka, pp: 328-332.
  • Nielsen A. 2020. Practical Time Series Analysis: Prediction with Statistics and Machine Learning; O’Reilly: Sebastopol CA USA.
  • Pham TT, Dang HL, Pham NTA, Dang HD. 2021. Adoption of contract farming for managing agricultural risks: A case study in rice production in the Mekong Delta Vietnam. J Agribus Develop Emerg Econ, 0839. https://doi.org/10.1108/JADEE-05-2021-0107.
  • Shastri S, Singh K, Kumar S, Kour P, Mansotra V. 2020. Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study. Chaos Solitons Fractals, 140: 110227. https://doi.org/10.1016/j.chaos.2020.110227.
  • Skendžić S, Zovko M, Živković IP, Lešić V, Lemić D. 2021. The impact of climate change on agricultural insect pests. Insects 12(5): 440. https://doi.org/10.3390/insects12050440.
  • Torres J, Hadjout D, Sebaa A, Martínez-Álvarez F, Troncoso A. 2020. Deep learning for time series forecasting: A survey. Big Data, 9: 3-21. 10.1089/big.2020.0159.
  • Wang K, Li K, Zhou L, Hu Y, Zhongyao C, Liu J, Chen C. 2019. Multiple convolutional neural networks for multivariate time series prediction. Neurocomputing. 360: 107-119. 10.1016/j.neucom.2019.05.023.
  • Wibawa A, Putra UA, Elmunsyah H, Pujianto U, Dwiyanto F, Hernandez L. 2022. Time-series analysis with smoothed Convolutional Neural Network. J Big Data, 9: 44. 10.1186/s40537-022-00599-y.
  • Yadav A, Jha CK, Sharan A. 2020. Optimizing LSTM for time series prediction in Indian stock market. Procedia Comput Sci, 167: 2091-2100. https://doi.org/10.1016/j.procs.2020.03.257.
  • Zou G, Guo Z Sun S, Jin Q. 2023. A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction. Appl Ener, 344: 121249.

Comparative Analysis of CNN, LSTM And Random Forest for Multivariate Agricultural Price Forecasting

Year 2023, , 422 - 426, 01.07.2023
https://doi.org/10.47115/bsagriculture.1304625

Abstract

Time series forecasting is an important research topic among agriculture economics. Especially, multivariate, multi-step and multiple output prediction tasks pose a challenge in research as their nature requires the investigation of intra- and inter-series correlation. The common statistical methods like ARIMA and SARIMA fall short in this kind of tasks. Deep learning architectures like Convolutional Neural Networks and Long Short-Term Memory networks are quite good at modelling the structures of complex data relations. In this study, a new dataset is composed through manual collection of data from the Ministry of Commerce of Turkish Republic. The dataset contains daily trade volumes and prices of potato, onion and garlic, which are most commonly consumed products in Turkish cuisine. The data pertains to the period between January 1, 2018 and November 26, 2022 (1791 days). A simple CNN and LSTM architectures as well Random Forest machine learning method are used to predict the next 10-day prices of the products. Accordingly, three models provided acceptable results in the prediction tasks, while CNN yielded by far the best result (MAE: 0.047, RMSE: 0.070).

References

  • Bayona-Oré S, Cerna R, Tirado Hinojoza E. 2021. Machine learning for price prediction for agricultural products. Wseas Transact Busin Econ, URL: https://doi.org/10.37394/23207.2021.18.92 (accessed date: March 11, 2022).
  • Bengio Y, Courville A, Vincent P. 2013. Representation learning: A review and new perspectives. IEEE Transact Pattern Anal Machine Intell, 35(8): 1798-1828. https://doi.org/10.1109/TPAMI.2013.50.
  • Casado-Vara R, Martin del Rey A, Pérez-Palau D, de-la-Fuente-Valentín L, Corchado J. M. 2021. Web traffic time series forecasting using LSTM neural networks with distributed asynchronous training. Math, 9(4): 421. https://doi.org/10.3390/math9040421.
  • Dharavath R, Khosla E. 2019. Seasonal ARIMA to forecast fruits and vegetable agricultural prices. In Proceedings of the IEEE International Symposium on Smart Electronic Systems, 16-18 December, Rourkela, India, pp: 47-52.
  • Dingli A, Fournier KS. 2017. Financial time series forecasting-a deep learning approach. Inter J Mach Learn Comput, 7(5): 118-122.
  • Duarte AMA, Gaglianone WP, de Carvalho Guillén OT, Issler JV. 2021. Commodity prices and global economic activity: a derived-demand approach. Energy Econ, 96: 105120. https://doi.org/10.1016/j.eneco.2021.105120.
  • Espinosa R, Palma J, Jiménez F, Kamińska J, Sciavicco G, Lucena-Sánchez E. 2021. A time series forecasting based multi-criteria methodology for air quality prediction. Appl Soft Comput, 113: 107850. https://doi.org/10.1016/j.asoc.2021.107850.
  • Freeman BS, Taylor G, Gharabaghi B, Thé J. 2018. Forecasting air quality time series using deep learning. J Air Waste Manage Assoc, 68(8): 866-886. https://doi.org/10.1080/10962247.2018.1459956.
  • Jebb AT, Tay L, Wang W, Huang Q. 2015. Time series analysis for psychological research: examining and forecasting change. Front Psychol, 6: 727.
  • Klompenburg T, Kassahun A, Catal C. 2020. Crop yield prediction using machine learning: A systematic literature review. Comput Electron Agri, 177. 105709. 10.1016/j.compag.2020.105709.
  • Kırbaş İ, Sözen A, Tuncer A. D, Kazancıoğlu F. Ş. 2020. Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA NARNN and LSTM approaches. Chaos Solitons Fractals, 138: 110015. https://doi.org/10.1016/j.chaos.2020.110015.
  • Madaan L, Sharma A, Khandelwal P, Goel S, Singla P, Seth A. 2019. Price forecasting anomaly detection for agricultural commodities in India. In Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies, July 3 – 5, Accra, Ghana, pp: 52-64.
  • Mili S, Bouhaddane M. 2021. Forecasting global developments and challenges in olive oil supply and demand: A Delphi survey from Spain. Agri, 11(3): 191. https://doi.org/10.3390/agriculture11030191.
  • Molitor K, Braun B, Pritchard B. 2017. The effects of food price changes on smallholder production and consumption decision‐making: evidence from Bangladesh. Geograp Res, 55(2): 206-216. https://doi.org/10.1111/1745-5871.12225.
  • Navaratnalingam S, Kodagoda N, Suriyawansa K. 2020. Exploiting multivariate LSTM models with multistep price forecasting for agricultural produce in Sri Lankan context. In Proceedings of 2nd International Conference on Advancements in Computing (ICAC), 10-11 December 2020, Colombo, Sri Lanka, pp: 328-332.
  • Nielsen A. 2020. Practical Time Series Analysis: Prediction with Statistics and Machine Learning; O’Reilly: Sebastopol CA USA.
  • Pham TT, Dang HL, Pham NTA, Dang HD. 2021. Adoption of contract farming for managing agricultural risks: A case study in rice production in the Mekong Delta Vietnam. J Agribus Develop Emerg Econ, 0839. https://doi.org/10.1108/JADEE-05-2021-0107.
  • Shastri S, Singh K, Kumar S, Kour P, Mansotra V. 2020. Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study. Chaos Solitons Fractals, 140: 110227. https://doi.org/10.1016/j.chaos.2020.110227.
  • Skendžić S, Zovko M, Živković IP, Lešić V, Lemić D. 2021. The impact of climate change on agricultural insect pests. Insects 12(5): 440. https://doi.org/10.3390/insects12050440.
  • Torres J, Hadjout D, Sebaa A, Martínez-Álvarez F, Troncoso A. 2020. Deep learning for time series forecasting: A survey. Big Data, 9: 3-21. 10.1089/big.2020.0159.
  • Wang K, Li K, Zhou L, Hu Y, Zhongyao C, Liu J, Chen C. 2019. Multiple convolutional neural networks for multivariate time series prediction. Neurocomputing. 360: 107-119. 10.1016/j.neucom.2019.05.023.
  • Wibawa A, Putra UA, Elmunsyah H, Pujianto U, Dwiyanto F, Hernandez L. 2022. Time-series analysis with smoothed Convolutional Neural Network. J Big Data, 9: 44. 10.1186/s40537-022-00599-y.
  • Yadav A, Jha CK, Sharan A. 2020. Optimizing LSTM for time series prediction in Indian stock market. Procedia Comput Sci, 167: 2091-2100. https://doi.org/10.1016/j.procs.2020.03.257.
  • Zou G, Guo Z Sun S, Jin Q. 2023. A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction. Appl Ener, 344: 121249.
There are 24 citations in total.

Details

Primary Language English
Subjects Agricultural Engineering
Journal Section Research Articles
Authors

Cevher Özden 0000-0002-8445-4629

Early Pub Date June 29, 2023
Publication Date July 1, 2023
Submission Date May 28, 2023
Acceptance Date June 23, 2023
Published in Issue Year 2023

Cite

APA Özden, C. (2023). Comparative Analysis of CNN, LSTM And Random Forest for Multivariate Agricultural Price Forecasting. Black Sea Journal of Agriculture, 6(4), 422-426. https://doi.org/10.47115/bsagriculture.1304625

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