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Financial Time Series Forecasting Using Directed-Weighted Chunking SVMs

机译：使用加权加权分块支持向量机的财务时间序列预测

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摘要

Support vector machines (SVMs) are a promising alternative to traditional regression estimation approaches. But, when dealing with massive-scale data set, there exist many problems, such as the long training time and excessive demand of memory space. So, the SVMs algorithm is not suitable to deal with financial time series data. In order to solve these problems, directed-weighted chunking SVMs algorithm is proposed. In this algorithm, the whole training data set is split into several chunks, and then the support vectors are obtained on each subset. Furthermore, the weighted support vector regressions are calculated to obtain the forecast model on the new working data set. Our directed-weighted chunking algorithm provides a new method of support vectors decomposing and combining according to the importance of chunks, which can improve the operation speed without reducing prediction accuracy. Finally, IBM stock daily close prices data are used to verify the validity of the proposed algorithm.
机译：支持向量机（SVM）是传统回归估计方法的有希望的替代方法。但是，在处理大规模数据集时，存在许多问题，例如训练时间长和存储空间需求过大。因此，SVM算法不适合处理金融时间序列数据。为了解决这些问题，提出了定向加权分块支持向量机算法。在该算法中，将整个训练数据集分为几个块，然后在每个子集上获得支持向量。此外，加权支持向量回归被计算以获得关于新工作数据集的预测模型。我们的有向加权分块算法提供了一种根据块的重要性分解和合并支持向量的新方法，可以在不降低预测精度的情况下提高运算速度。最后，使用IBM股票每日收盘价数据来验证所提出算法的有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2014年第8期|170424.1-170424.7|共7页
  • 作者

    Yongming Cai; Lei Song; Tingwei Wang; Qing Chang;

  • 作者单位

    School of Management, University of Jinan, Jinan 250002, China;

    School of Management, University of Jinan, Jinan 250002, China;

    School of Management, University of Jinan, Jinan 250002, China;

    School of Management, Inner Mongolia University of Technology, Huhhot 010050, China;

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