@inproceedings{71275eff9f0e43178a31754766444c15,
title = "Comparing ML Models for Food Production Forecasting",
abstract = "Food production forecasting is a challenge to decision-makers at agriculture authorities. In this study, we compare the performance of three different Machine Learning (ML) approaches for predicting the production of food items. Particularly, we compare Long Short-Term Memory (LSTM) that can handle sequence-based data, such as time-series data, against classical machine learning time-series analysis models, such as Auto Regression (AR) and Auto-Regressive Integrated Moving Average (ARIMA). The algorithms are incorporated into a forecasting tool to perform a periodic prediction of food production. The results show that different algorithms can work better in different datasets, with the LSTM approach being more flexible that can be further improved.",
keywords = "ARIMA, LSTM, Time-series forecasting",
author = "Nouf Alkaabi and Siddhartha Shakya",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022 ; Conference date: 13-12-2022 Through 15-12-2022",
year = "2022",
doi = "10.1007/978-3-031-21441-7\_22",
language = "British English",
isbn = "9783031214400",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "303--308",
editor = "Max Bramer and Frederic Stahl",
booktitle = "Artificial Intelligence XXXIX - 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Proceedings",
address = "Germany",
}