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.
| Original language | British English |
|---|---|
| Title of host publication | 10th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2023 |
| ISBN (Electronic) | 9798400704734 |
| DOIs | |
| State | Published - 4 Dec 2023 |
| Event | 10th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2023 - Taormina, Italy Duration: 4 Dec 2023 → 7 Dec 2023 |
Publication series
| Name | 10th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2023 |
|---|
Conference
| Conference | 10th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2023 |
|---|---|
| Country/Territory | Italy |
| City | Taormina |
| Period | 4/12/23 → 7/12/23 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 2 Zero Hunger
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