Affiliation:
1. Department of Informatics and Telematics, Harokopio University of Athens, 17778 Tavros, Greece
2. Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
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.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
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