Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network

Krzysztof Lutosławski · Marcin Hernes · Joanna Radomska · Monika Hajdas · Ewa Walaszczyk · Agata Kozina

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Abstract

Food demand prediction is a significant issue for both businesses processes improvement and sustainable development issues. The data science methods, including artificial intelligence methods, are often used for this purpose. The aim of this research is to develop the models for food demand prediction based on the Nonlinear Autoregressive Exogenous Neural Network. The research focuses on the processed food, such as bread or butter. Developed models’ architectures differing in the number of hidden layers and the number of neurons in the hidden layers, as well as with different sizes of the delay-line, were tested for a given product. Results of the research show that depending on the type of product, prediction performance slightly differed. The results of the R2 measure ranged from 96,2399 to 99,6477 depending on particular products. The proposed models can be used in a company’s intelligent management system for rational control of inventories and food production. It can also lead to reducing food waste.

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2018Forecasting of demand using ARIMA model 2016Data Science in Action 2017A deep learning ensemble approach for crude oil price forecasting 2018Exploring the use of deep neural networks for sales forecasting in fashion retail 2015Performance of state space and ARIMA models for consumer retail sales forecasting 2014‘Horses for Courses’ in demand forecasting 2019An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain 2015A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting

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