Gorkem SariyerCeren ÖcalÖcal, CerenSariyer, Görkem2025-10-0620209781799825814, 97817998258219781799825821978179982581410.4018/978-1-7998-2581-4.ch0022-s2.0-85091886939https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091886939&doi=10.4018%2F978-1-7998-2581-4.ch002&partnerID=40&md5=af1ba7fc9d656ef20bf2f20840d98772https://gcris.yasar.edu.tr/handle/123456789/9236https://doi.org/10.4018/978-1-7998-2581-4.ch002In this study linear regression and neural network-based hybrid models are developed for modelling the daily ED visits. Month and week of the year day of the week and period of the day are used as input variables of the linear regression model. Generated forecasts and the residuals are further processed through a multilayer perceptron model to improve the performance of forecasting. To obtain forecasts for daily number of patient visits aggregation is used where the obtained periodical forecasts are summed up. By comparing the performances of models in generating periodical and dailyforecasts this chapter not only shows that hybrid model improves the forecasting performance significantly but also aggregation fits well in practice. © 2022 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessBook Part