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Because of globalization, fast changes of technology and short life cycle of products, enhancing the accuracy of demand forecasts becomes one of the important issues for managers. The objective of this paper is to analyze and explore given data of orders using adaptive neuro-fuzzy inference system (ANFIS) and to draw up, by ANFIS learning mechanism, the relational rules from historical order data,...
The rapid development of automobile industry in China promotes the stable growth of the automotive aftermarket. For optimizing supply chain operations and reducing costs, it is critical for a company to forecast the demands for auto spare parts in the future. This paper proposes an improved Regression-Bayesian-BBNN (RBBPNN) based model to realize the demands forecasting. Compared with a classic ARMA...
Short-term electric load modeling and forecasting has been intensively studied during the past 50 years. With the emerging development of smart grid technologies, demand side management (DSM) starts to attract the attention of electric utilities again. To perform a decent DSM, beyond when and how much the demand will be, the utilities are facing another question: why is the electricity being consumed?...
Stock market predictions comprise challenging applications of modern time series forecasting and are essential to the success of many businesses and financial institutions. In this paper, a novel nonlinear combination model is presented for stock market forecasting, which based on Support Vector Machine (SVM) regression combining the linear regression of traditional statistical model with the nonlinear...
This paper introduces a novel customers' demand forecasting model based on least squares support vector machines (LS-SVM) for e-business enterprises. Firstly, the paper presents actual state of e-business, and discusses some factors that block e-business advance in China. Then, some common techniques used for forecasting are briefly reviewed together with their shortcomings respectively. To solve...
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