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- W2899821837 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." @default.
- W2899821837 created "2018-11-16" @default.
- W2899821837 creator A5085093267 @default.
- W2899821837 date "2018-09-17" @default.
- W2899821837 modified "2023-09-26" @default.
- W2899821837 title "Time series forecasting of hospital Inpatients and Day case waiting list using ARIMA, TBATS and Neural Network Models" @default.
- W2899821837 hasPublicationYear "2018" @default.
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