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Weather Sensitive Demand is defined as abnormal variation of demand from seasonal fluctuation because of weather condition's abnormal fluctuation. The majority of retailers acknowledge the impacts of weather. However, none of the conventional predictive modeling processes adequately address the impact of weather. In this paper, a weather sensitive demand forecasting method based on support vector...
Product substitution is a phenomenon which occurs when the product is out of stock, and it distorts the true demand for the product while reducing retailers' stock-out losses and improving service levels at the same time, that is expanding the demand for substitutable products. Therefore, how to estimate the product substitution is a key to improve the demand forecasting accuracy. Based on this, a...
In view of the complex time-varing signal pattern recognition, the method of water flooded layer recognition based on wavelet packet transform and support vector machine had been proposed in this paper. According to the multi-resolution characteristics of wavelet packet analysis, the denoising for signals and the extraction of useful signals had been presented. The sample space had been mapped into...
An accurate demand forecasting model has academic and practical significance to supply chain management for China's retail industry. In this paper, a novel demand forecasting model named WFSSVM (Wrapper Feature Selection optimized SVM) is proposed. Genetic algorithm based wrapper feature selection method is firstly employed to analyze the sales data of a kind product (including various kinds of brand)...
Neural networks ensemble is a promising tool in the field of structure-activity relationship (SAR). Based on support vector machine (SVM), a new method called RRSE (rough reducts based SVM ensemble) is employed to discriminate between high and low activities of ethofenprox analogous based on the molecular descriptors. By using RRSE, individual SVMs of ensemble model are constructed by projection of...
Statistical learning theory is for small-sample statistics. And support vector machine is a new machine learning method based on the statistical learning theory. The support vector machine not only has solved certain problems in many learning methods, such as small sample, over fitting, high dimension and local minimum, but also has a higher generalization (forecasting) ability than that of artificial...
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