A Novel Efficient Adaptive Sliding Window Model for Week-ahead Price Forecasting

ZHU Quan-yin, YIN Yong-hu, YAN Yun-yang, GU Tian-feng


In order to improve the accuracy of price forecasting by Web extracting, a novel efficient improved Adaptive Sliding Window (ASW) that the coefficients of the window width can be auto adjusts is proposed in this paper. Agricultural products price based on ASW is utilized to verify validity of adaptive Back Propagation (BP) neural network and adaptive Radial Basis Function (RBF) neural network model respectively. Experiments demonstrated that the Mean Absolute Error (MAE) on ASW model can be getting 99.62 percent accuracy rate. Experiment results proved that the proposed ASW model and adaptive BP neural network model are meaningful and useful to analyze and to research products market, but the proposed ASW model is the best one because of its speed is the fast one which can save time 80 percent than the adaptive BP neural network.


DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4490


Price forecasting; Agricultural products; Adaptive sliding window; Adaptive BP neural network; Adaptive RBF neural network

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