In this research, we propose efficient demand forecasting scheme for intermittent demand. For this purpose, we first extensively analyze the drawbacks of the existing forecasting methods such as Croston method and Syntetos-Boylan approximation, then using these findings we propose the new demand forecasting method. Our goal is to develop forecasting method robust across many situations, not necessarily optimal for a limited number of specific situations. For this end, we adopt combining forecasting method that utilizes unbiased forecasting methods such as simple exponential smoothing and simple moving average. Various simulation results show that the proposed forecasting method performed better than the existing forecasting methods.

A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.