Date Available
4-28-2017
Year of Publication
2017
Document Type
Master's Thesis
Degree Name
Master of Science in Mechanical Engineering (MSME)
College
Engineering
Department/School/Program
Mechanical Engineering
Advisor
Dr. Wei Li
Abstract
Time series forecasting and modeling are challenging problems during the past decades, because of its plenty of properties and underlying correlated relationships. As a result, researchers proposed a lot of models to deal with the time series. However, the proposed models such as Autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs) only describe part of the properties of time series. In this thesis, we introduce a new hybrid model integrated filter structure to improve the prediction accuracy. Case studies with real data from University of Kentucky HealthCare are carried out to examine the superiority of our model. Also, we applied our model to operating room (OR) to reduce the inefficiency cost. The experiment results indicate that our model always outperforms compared with other models in different conditions.
Digital Object Identifier (DOI)
https://doi.org/10.13023/ETD.2017.201
Recommended Citation
Pan, Xinwei, "FORECASTING THE WORKLOAD WITH A HYBRID MODEL TO REDUCE THE INEFFICIENCY COST" (2017). Theses and Dissertations--Mechanical Engineering. 91.
https://uknowledge.uky.edu/me_etds/91
