Utilization of Whittaker-Henderson Smoothing Method for Improving Neural Network Forecasting Accuracy
Abstract
Health institutions need to ensure the availability of drug stocks for patients. There are challenges related to the uncertainty of the amount of drug use for the next period. Uncertainty can be reduced by analysing historical drug data to predict future demand. Time series can contain spikes or fluctuation pattern which spikes can disguise the main information. Hence, it can affect the accuracy of the prediction model. One widely used forecasting method in the time series data is the artificial neural network (ANN) method. The ANN method requires the pre-processing stage of the data before the training process. The pre-processing stage is essential to obtain information or knowledge. This study focused on applying smoothing methods at the pre-processing stage of the ANN method. The application of the smoothing method was expected to improve the quality of ANN learning data that would lead to better predictive accuracy. This research focuses on implementing the smoothing method in data pre-processing step for ANN method. Smoothing methods used in this research were exponential smoothing (ES) and Whittaker-Henderson (WH) smoothing applied to two time series datasets. The refining method used in this study was the WH method, which was tested on two time series datasets of medicine. The results show that the mean square error (MSE) obtained by applying the WH method was lower than the non-smoothing ANN for both datasets. Evaluation results revealed that implementing WH smoothing method in data pre-processing step for ANN (WH+ANN) provided MSE significantly lower than ANN results with a confidence level of 94% for dataset 1 and 85% for the dataset 2.
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