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Based on Genetic Optimization of Support Vector Machine Consumer Price Index Forecast

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The consumer price index (CPI) trends correctly predict, will help management to develop consumer price policies, wage policy, national economic development strategy. There are many researchers for the past consumer price index (CPI) for a lot of research and achieved many results. Most forecasting methods use a simple neural network method, it is difficult to obtain satisfactory predictions. In this paper, least squares support vector machines and genetic simulated annealing algorithm is described, given the genetic simulated annealing algorithm to optimize the least squares support vector machine model. Through simulation experiments, the consumer price index (CPI) tends to be a case study, the results show that: genetic simulated annealing algorithm to optimize the least squares support vector machine model has higher prediction accuracy.
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Document Type: Research Article

Publication date: March 1, 2012

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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