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Titlebook: Computational Intelligence in Economics and Finance; Shu-Heng Chen,Paul P. Wang Book 2004 Springer-Verlag Berlin Heidelberg 2004 Artificia

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书目名称Computational Intelligence in Economics and Finance
编辑Shu-Heng Chen,Paul P. Wang
视频video
概述Includes supplementary material:
丛书名称Advanced Information Processing
图书封面Titlebook: Computational Intelligence in Economics and Finance;  Shu-Heng Chen,Paul P. Wang Book 2004 Springer-Verlag Berlin Heidelberg 2004 Artificia
描述.Due to the ability to handle specific characteristics of economics and finance forecasting problems like e.g. non-linear relationships, behavioral changes, or knowledge-based domain segmentation, we have recently witnessed a phenomenal growth of the application of computational intelligence methodologies in this field...In this volume, Chen and Wang collected not just works on traditional computational intelligence approaches like fuzzy logic, neural networks, and genetic algorithms, but also examples for more recent technologies like e.g. rough sets, support vector machines, wavelets, or ant algorithms. After an introductory chapter with a structural description of all the methodologies, the subsequent parts describe novel applications of these to typical economics and finance problems like business forecasting, currency crisis discrimination, foreign exchange markets, or stock markets behavior..
出版日期Book 2004
关键词Artificial Intelligence; Business Forecasting; Finance; Financial Data Mining; Financial Engineering; Pat
版次1
doihttps://doi.org/10.1007/978-3-662-06373-6
isbn_softcover978-3-642-07902-3
isbn_ebook978-3-662-06373-6
copyrightSpringer-Verlag Berlin Heidelberg 2004
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Forecasting the Opening Cash Price Index in Integrating Grey Forecasting and Neural Networks: Evidenl) Taiwan futures contracts and its underlying cash market during the non-cash-trading (NCT) period. Previous day’s cash market closing index and the grey forecasts by using the futures during the NCT period are used to forecast the 09:00 AM opening cash price index by the neural networks model. To
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Financial Applications of Wavelets and Self-organizing Mapsthe denoising of the stock time-series wavelet packets are used because of their optimal signal compression and denoising capabilities. The visualisation of transient shocks like crashes, in higher order wavelet coefficients is presented. The Self-organising Map Neural Network is introduced to aid t
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Pattern Matching in Multidimensional Time Seriession, that conforms to the PDL, creates a non deterministic pattern matching machine (PMM) that can be used as a searching device for detecting sequential patterns or functional (statistical) relationships in multidimensional data. As an example, a chart pattern of ex ante unknown length is encoded
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