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Combining Time-Scale Feature Extractions with SVMs for Stock Index Forecasting

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Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4234))

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Abstract

Support vector machine (SVM) has appeared as a powerful tool for time series forecasting and demonstrated better performance over other methods. This paper proposes a novel hybrid model which combines time-scale feature extractions with SVM models for stock index forecasting. The time series of explanatory variables are decomposed by the wavelet basis, and the extracted time-scale features then serve as inputs of a SVM model which performs the nonparametric forecasting. Compared with pure SVM models or traditional GARCH models, the performance of the new method is the best. The root-mean-squared forecasting errors are significantly reduced. The results of this study can help investors for controlling and reducing their risks in international investments.

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© 2006 Springer-Verlag Berlin Heidelberg

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Huang, SC., Wang, HW. (2006). Combining Time-Scale Feature Extractions with SVMs for Stock Index Forecasting. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_44

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  • DOI: https://doi.org/10.1007/11893295_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46484-6

  • Online ISBN: 978-3-540-46485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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