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Tamatta, Raj Kumar (2018) Time series forecasting of hospital Inpatients and Day case waiting list using ARIMA, TBATS and Neural Network Models. Masters thesis, Dublin, National College of Ireland.
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Abstract
This research aims to provide the forecasting of patients waiting list in different time band over all Ireland's Hospitals. The NTPF collect and manage waiting list data of patients from all hospitals of Ireland. The data is used to build forecasting model, first data is decomposed to identify the pattern of on time series like trending, seasonality, variation and de-seasonalize to remove irregularity and noise from time series data. Based on related work and literature reviews, all relevant time series algorithms are evaluated to choose best time series model. The ARIMA, TBATS and AR-NN are suitable algorithm for this research which is further compare with least RMSE value. The performance of each algorithm is depended on different time series data. The ARIMA, TBATS and AR-NN model were applied on five different time series forecasting cases like overall waiting list of patients, waiting list of patients in different 0-3, 3-6, 6-12 and 12 + time band. The performance of ARIMA model found to be better in most of the case, where time series data has constant seasonal pattern, TBATS model is better where data having irregular seasonal pattern. As this is linear time series problem the performance of AR-NN neural network model not great. In overall comparison of all three models, ARIMA is the best model with least RMSE value.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > Healthcare Industry |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Caoimhe Ní Mhaicín |
| Date Deposited: | 05 Nov 2018 14:03 |
| Last Modified: | 05 Nov 2018 14:03 |
| URI: | https://norma.ncirl.ie/id/eprint/3432 |
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