Comparing ML Models for Food Production Forecasting

Nouf Alkaabi, Siddhartha Shakya

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageBritish English
Title of host publicationArtificial Intelligence XXXIX - 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Proceedings
EditorsMax Bramer, Frederic Stahl
PublisherSpringer Science and Business Media Deutschland GmbH
Pages303-308
Number of pages6
ISBN (Print)9783031214400
DOIs
StatePublished - 2022
Event42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022 - Cambridge, United Kingdom
Duration: 13 Dec 202215 Dec 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13652 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022
Country/TerritoryUnited Kingdom
CityCambridge
Period13/12/2215/12/22

Keywords

  • ARIMA
  • LSTM
  • Time-series forecasting

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