Predicting Consumer Service Price Evolution during the COVID-19 Pandemic: An Optimized Machine Learning Approach

Abstract

This research analyzes the impact of the COVID-19 pandemic on consumer service pricing within the European Union, focusing on the Transportation, Accommodation, and Food Service sectors. Our study employs various machine learning models, including multilayer perceptron, XGBoost, CatBoost, and random forest, along with genetic algorithms for comprehensive hyperparameter tuning and price evolution forecasting. We incorporate coronavirus cases and deaths as factors to enhance prediction accuracy. The dataset comprises monthly reports of COVID-19 cases and deaths, alongside managerial survey responses regarding company estimations. Applying genetic algorithms for hyperparameter optimization across all models results in significant enhancements, yielding optimized models that exhibit RMSE score reductions ranging from 3.35% to 5.67%. Additionally, the study demonstrates that XGBoost yields more accurate predictions, achieving an RMSE score of 17.07.