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This study presents a hybrid data mining model for stock price that combines wavelet transform and support vector machines. In our proposed method, the wavelet transform is firstly applied to eliminate the noise of the stock time series. Secondly, the stepwise regression is employed in feature selection. Thirdly, the time delay concept is utilized to obtain the optimum prediction model. For illustration...
With the rise of e-commerce business, sales forecasting plays an increasingly important role, for accurate and speedy forecasting can help e-commerce companies solve all the uncertainty associated with demand and supply and reduce inventory cost. As the rapid growth in the amount of data, traditional intelligence models like Neural Networks have weakness in terms of speed. In this paper, we introduce...
Web information, regarded as a useful repository including abundant data and knowledge, attracts much attention from researchers and practitioners, and has been used to analyze and forecast economic and social hotspots in recent years. In this paper, a novel neural network based forecasting method is proposed for the unemployment rate prediction using search engine query data. The empirical results...
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