research-article

Strengthening Food Security: A Comparison of Food Import Forecasting Models

Authors:

Siddhartha Shakya,

Himadri Khargharia,

Aysha Ali Saif Al Shamisi,

Asma AlawnehAuthors Info & Claims

BDCAT '23: Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies

Article No.: 10, Pages 1 - 6

https://doi.org/10.1145/3632366.3632392

Published: 03 April 2024 Publication History

Abstract

Food security relies on factors like availability, access, and stability, often assisted by food imports when local production falters. Importantly, these imports stabilize supplies, mitigate shortages and price volatility, and enhance economic stability. Anticipating import requirements is vital for proactive food security planning. In this case study, we employ multiple forecasting models to predict food import for a large number of products from multiple countries. The results highlight varying algorithm performance across datasets. Traditional statistical models remain highly competitive compared to newer alternatives, especially for shorter time series. Our study introduces a multi-model forecasting approach to predict periodic food imports, a pivotal tool for food authorities.

References

[1]

[n. d.]. scikit-learn, Machine Learning in Python. https://scikit-learn.org/stable/. Accessed: 2023-06-14.

[2]

[n. d.]. sktime, a library for time series analysis in Python. http://www.sktime.net/en/latest/. Accessed: 2023-06-14.

[3]

Nouf Alkaabi and Siddhartha Shakya. 2022. Comparing ML Models for Food Production Forecasting. In Artificial Intelligence XXXIX: 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Cambridge, UK, December 13--15, 2022, Proceedings. Springer, 303--308.

Digital Library

[4]

Vassilis Assimakopoulos and Konstantinos Nikolopoulos. 2000. The theta model: a decomposition approach to forecasting. International journal of forecasting 16, 4 (2000), 521--530.

[5]

Jon Louis Bentley. 1975. Multidimensional binary search trees used for associative searching. Commun. ACM 18, 9 (1975), 509--517.

Digital Library

[6]

Léon Bottou. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade: Second Edition. Springer, 421--436.

[7]

Leo Breiman. 2001. Random forests. Machine learning 45 (2001), 5--32.

[8]

Evita Choiriyah, Utami Dyah Syafitri, and I Made Sumertajaya. 2020. PENGEM-BANGAN MODEL PERAMALAN SPACE TIME: Studi Kasus: Data Produksi Padi di Sulawesi Selatan. Indonesian Journal of Statistics and Its Applications 4, 4 (2020), 579--589.

[9]

Robert B Cleveland, William S Cleveland, Jean E McRae, and Irma Terpenning. 1990. STL: A seasonal-trend decomposition. J. Off. Stat 6, 1 (1990), 3--73.

[10]

David Corney. 2000. Designing food with bayesian belief networks. In Evolutionary Design and Manufacture: Selected Papers from ACDM'00. Springer, 83--94.

[11]

John D Croston. 1972. Forecasting and stock control for intermittent demands. Journal of the Operational Research Society 23, 3 (1972), 289--303.

[12]

Xin Dang, Hanxiang Peng, Xueqin Wang, and Heping Zhang. 2008. Theil-sen estimators in a multiple linear regression model. Olemiss Edu (2008).

[13]

Alysha M De Livera, Rob J Hyndman, and Ralph D Snyder. 2011. Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American statistical association 106, 496 (2011), 1513--1527.

[14]

NHS England and NHS Improvement. 2020. Advanced forecasting techniques. NHS. London: NHS England (2020).

[15]

Jamal Fattah, Latifa Ezzine, Zineb Aman, Haj El Moussami, and Abdeslam Lachhab. 2018. Forecasting of demand using ARIMA model. International Journal of Engineering Business Management 10 (2018), 1847979018808673.

[16]

Jerome Friedman, Trevor Hastie, and Rob Tibshirani. 2010. Regularization paths for generalized linear models via coordinate descent. Journal of statistical software 33, 1 (2010), 1.

[17]

Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 249--256.

[18]

Geoffrey E Hinton. 1990. Connectionist learning procedures. In Machine learning. Elsevier, 555--610.

[19]

Rob J Hyndman and George Athanasopoulos. 2018. Forecasting: principles and practice. OTexts.

[20]

Rob J Hyndman and Yeasmin Khandakar. 2008. Automatic time series forecasting: the forecast package for R. Journal of statistical software 27 (2008), 1--22.

[21]

II Ismagilov and SF Khasanova. 2016. Algorithms of parametric estimation of polynomial trend models of time series on discrete transforms. Academy of Strategic Management Journal 15 (2016), 20.

[22]

Bent Jørgensen. 1992. The theory of exponential dispersion models and analysis of deviance. (1992).

[23]

Prajakta S Kalekar et al. 2004. Time series forecasting using holt-winters exponential smoothing. Kanwal Rekhi school of information Technology 4329008, 13 (2004), 1--13.

[24]

Saeed Khaki and Lizhi Wang. 2019. Crop yield prediction using deep neural networks. Frontiers in plant science 10 (2019), 621.

[25]

Himadri Sikhar Khargharia, Roberto Santana, Siddhartha Shakya, Russell Ainslie, and Gilbert Owusu. 2020. Investigating RNNs for vehicle volume forecasting in service stations. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2625--2632.

[26]

Himadri Sikhar Khargharia, Siddhartha Shakya, Russell Ainslie, Sara AlShizawi, and Gilbert Owusu. 2019. Predicting demand in IoT enabled service stations. In 2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA). IEEE, 81--87.

[27]

Himadri Sikhar Khargharia, Siddhartha Shakya, Russell Ainslie, and Gilbert Owusu. 2021. Evolving large scale prediction models for vehicle volume forecasting in service stations. In Artificial Intelligence XXXVIII: 41st SGAI International Conference on Artificial Intelligence, AI 2021, Cambridge, UK, 2021, Proceedings 41. Springer, 224--238.

Digital Library

[28]

Himadri Sikhar Khargharia, Siddhartha Shakya, Sara Sharif, Russell Ainslie, and Gilbert Owusu. 2022. On Predicting the Work Load for Service Contractors. In Artificial Intelligence XXXIX: 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Cambridge, UK, December 13--15, 2022, Proceedings. Springer, 223--237.

Digital Library

[29]

Seung-Jean Kim, Kwangmoo Koh, Michael Lustig, Stephen Boyd, and Dimitry Gorinevsky. 2007. An interior-point method for large-scale \ell_1-regularized least squares. IEEE journal of selected topics in signal processing 1, 4 (2007), 606--617.

[30]

David JC MacKay. 1992. Bayesian interpolation. Neural computation 4, 3 (1992), 415--447.

[31]

David JC MacKay et al. 1994. Bayesian nonlinear modeling for the prediction competition. ASHRAE transactions 100, 2 (1994), 1053--1062.

[32]

Sunali Mahajan, Manish Sharma, and Amit Gupta. 2020. ARIMA modelling for forecasting of rice production: A case study of India. Agricultural Science Digest-A Research Journal 40, 4 (2020), 404--407.

[33]

Ganapathy Mahalakshmi, S Sridevi, and Shyamsundar Rajaram. 2016. A survey on forecasting of time series data. In 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16). IEEE, 1--8.

[34]

Amal Mahmoud and Ammar Mohammed. 2021. A survey on deep learning for time-series forecasting. Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges (2021), 365--392.

[35]

P McCullagh and J Nelder. 1989. Generalized Linear Models Second edition Chapman & Hall.

[36]

AB Owen. 2006. A robust hybrid of lasso and ridge regression Technical Report.

[37]

Carl Edward Rasmussen, Christopher KI Williams, et al. 2006. Gaussian processes for machine learning. Vol. 1. Springer.

[38]

SANTOSHA Rathod, KN Singh, SG Patil, Ravindrakumar H Naik, MRINMOY Ray, and Vikram Singh Meena. 2018. Modeling and forecasting of oilseed production of India through artificial intelligence techniques. Indian J. Agric. Sci 88, 1 (2018), 22--27.

[39]

Ryan M Rifkin and Ross A Lippert. 2007. Notes on regularized least squares. (2007).

[40]

Elvezio M Ronchetti and Peter J Huber. 2009. Robust statistics. John Wiley & Sons Hoboken, NJ, USA.

[41]

Pawan Kumar Sharma, Sudhakar Dwivedi, Lyaqat Ali, and RK Arora. 2018. Forecasting maize production in India using ARIMA model. Agro-Economist 5, 1 (2018), 1--6.

[42]

Yu Sheng and Ligang Song. 2019. Agricultural production and food consumption in China: A long-term projection. China Economic Review 53 (2019), 15--29.

[43]

Feng Song, Junxu Liu, Tingting Zhang, Jing Guo, Shuran Tian, and Dang Xiong. 2020. The grey forecasting model for the medium-and long-term load forecasting. In Journal of Physics: Conference Series, Vol. 1654. IOP Publishing, 012104.

[44]

Jan Mei Soon. 2020. Application of bayesian network modelling to predict food fraud products from China. Food Control 114 (2020), 107232.

[45]

Tomislav Sudarevic, Predrag Radojevic, Darko Marjanovic, and Radovan Dragas. 2017. Marketing and financial barriers in agri-food exporting. British food journal (2017).

[46]

Sean J Taylor and Benjamin Letham. 2018. Forecasting at scale. The American Statistician 72, 1 (2018), 37--45.

[47]

Yoshimasa Tsuruoka, Jun'ichi Tsujii, and Sophia Ananiadou. 2009. Stochastic gradient descent training for l1-regularized log-linear models with cumulative penalty. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 477--485.

[48]

Wei Biao Wu and Zhibiao Zhao. 2007. Inference of trends in time series. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69, 3 (2007), 391--410.

[49]

Hui Zou, Trevor Hastie, and Robert Tibshirani. 2007. On the Degrees of Freedom of the Lasso. Annals of statistics 35, 5 (2007), 2173--2192.

Cited By

Kumar MKrishnammal NGupta MBegum MSultana SDegala D(2025)Sustainable Agriculture in Food Security Integrating Satellite Data Risk Assessment by Cyberattack Detection: AI ApplicationsRemote Sensing in Earth Systems Sciences10.1007/s41976-025-00194-8Online publication date: 13-Feb-2025
https://doi.org/10.1007/s41976-025-00194-8
Truchan HKalfar CAhmadi ZSerra ESpezzano F(2024)LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for the Long-term Time Series ForecastingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679527(2271-2281)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679527

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Strengthening Food Security: A Comparison of Food Import Forecasting Models

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cover image ACM Conferences

BDCAT '23: Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies

December 2023

187 pages

ISBN:9798400704734

DOI:10.1145/3632366

Co-chairs:
Omer Rana,
Massimo Villari,
Program Co-chairs:
Geoffrey Fox,
Maria Fazio

Copyright © 2023 ACM.

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Published: 03 April 2024

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BDCAT '23: IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies

December 4 - 7, 2023

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Cited By

Kumar MKrishnammal NGupta MBegum MSultana SDegala D(2025)Sustainable Agriculture in Food Security Integrating Satellite Data Risk Assessment by Cyberattack Detection: AI ApplicationsRemote Sensing in Earth Systems Sciences10.1007/s41976-025-00194-8Online publication date: 13-Feb-2025
https://doi.org/10.1007/s41976-025-00194-8
Truchan HKalfar CAhmadi ZSerra ESpezzano F(2024)LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for the Long-term Time Series ForecastingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679527(2271-2281)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679527

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