Food Demand Forecasting Using Machine Learning

M. Jayamma, R. Akib Hussain, P. Hemanth Kumar, M. Anil Kumar, A. Prasanth, S. Prasharshavaradhan

2025

Abstract

Food Demand forecasting is a process that has to be done by food and beverage companies to ensure that they manage their supply chains efficiently and have minimum food wastage and optimum inventory levels. Accurate demand prediction can help businesses to meet customer expectations, reduce operating expenses, and prevent stockout or overstocking. The final universal machine learning based system is developed from the systemic insights gained through the study of past sales data, weather, seasonality, promotion and holidays. There are some data preparation steps, cleaning, normalization, and some other statistical knowledge is used to extract dignified features for the prediction (called feature engineering). For demand prediction different machine learning algorithms are applied such as LSTM (Long Short-Term Memory networks), XGBoost, ARIMA (Auto Regressive Integrated Moving Average) and Linear regression. These models will be trained on past data, and evaluated on metrics like MAE (Mean Absolute Error), RMSE (Root Mean Square Error), and R2 Score. Thus, XGBoost is good in prediction by fitting to the data in a less continuous manner while LSTM effectively captures time series dependencies.

Paper Citation

in Harvard Style

Jayamma M., Hussain R., Kumar P., Kumar M., Prasanth A. and Prasharshavaradhan S. (2025). Food Demand Forecasting Using Machine Learning. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 565-569. DOI: 10.5220/0013886500004919

in Bibtex Style

@conference{icrdicct`2525,
author={M. Jayamma and R. Hussain and P. Kumar and M. Kumar and A. Prasanth and S. Prasharshavaradhan},
title={Food Demand Forecasting Using Machine Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={565-569},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013886500004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Food Demand Forecasting Using Machine Learning
SN - 978-989-758-777-1
AU - Jayamma M.
AU - Hussain R.
AU - Kumar P.
AU - Kumar M.
AU - Prasanth A.
AU - Prasharshavaradhan S.
PY - 2025
SP - 565
EP - 569
DO - 10.5220/0013886500004919
PB - SciTePress