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.

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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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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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