A Comparison of Advanced Machine Learning Models for Food Import Forecasting

Corrado Mio, Siddhartha Shakya

2024

Abstract

Food security is responsible for food availability, access and price stability. Food import is used to ensure availability when local production is inadequate and diversity when local production is not possible. Food import prediction is one of the tools used to ensure food security. In this case study, we analyze Neural Network Forecasting models applied to a food import dataset to understand whether these models, when applied to small time series, perform better than statistical or regression models. And if it is better to use short or long forecast horizons.

Paper Citation

in Harvard Style

Mio C. and Shakya S. (2024). A Comparison of Advanced Machine Learning Models for Food Import Forecasting. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA; ISBN 978-989-758-721-4, SciTePress, pages 568-575. DOI: 10.5220/0012998100003837

in Bibtex Style

@conference{ncta24,
author={Corrado Mio and Siddhartha Shakya},
title={A Comparison of Advanced Machine Learning Models for Food Import Forecasting},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA},
year={2024},
pages={568-575},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012998100003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA
TI - A Comparison of Advanced Machine Learning Models for Food Import Forecasting
SN - 978-989-758-721-4
AU - Mio C.
AU - Shakya S.
PY - 2024
SP - 568
EP - 575
DO - 10.5220/0012998100003837
PB - SciTePress