Forecasting hourly foodservice sales during geopolitical and economical disruption using zero-inflated mixed effects models
Nathan A. Judd, Kalliopi Mylona, Haiming Liu, Andy Hogg, Tim Butler
TL;DR
This paper introduces a statistical model to predict food sales during disruptions, using limited data to improve accuracy in volatile times.
Contribution
A novel zero-inflated mixed effects model is proposed for accurate sales forecasting with limited data during disruptions.
Findings
The model accurately predicts sales with limited data by accounting for random effects.
It reduces variance in fixed effect estimates, improving inference during volatile demand.
The model fits quickly using Monte Carlo likelihood approximation.
Abstract
Accurate predictions of product sales are essential to the foodservice sector, for planning and saving of resources. In this paper, a zero-inflated negative binomial mixed-effects model with several factors was used to predict the total sales of different product categories, taking into consideration different sites, time and weather conditions. It fits quickly by maximising the ordinary Monte Carlo likelihood approximation. The model succeeded in accurate predictions with limited data where the random effects fitted well to the exogenous factors that added noise to the dataset. This enabled an improved inference from the model by reducing the variance in the estimates of fixed effects used in the interpretation of the results. This shows how statistical modelling, using less data, can improve predictions in the foodservice industry during times of volatile demand.
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Taxonomy
TopicsForecasting Techniques and Applications · Market Dynamics and Volatility · Stock Market Forecasting Methods