Feature Selection for Support Vector Machines in Financial Time Series Forecasting

机译：金融时间序列预测中支持向量机的特征选择

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This paper deals with the application of saliency analysis to Support Vector Machines (SVMs) for feature selection. The importance of feature is ranked by evaluating the sensitivity of the network output to the feature input in terms of the partial derivative. A systematic approach to remove irrelevant features based on the sensitivity is developed. Five futures contracts are examined in the experiment. Based on the simulation results, it is shown that that saliency analysis is effective in SVMs for identifying important features.

机译：本文讨论了显着性分析在支持向量机（SVM）中进行特征选择的应用。通过根据偏导数评估网络输出对特征输入的敏感性来对特征的重要性进行排名。开发了一种基于灵敏度的去除不相关特征的系统方法。实验中检查了五种期货合约。从仿真结果可以看出，显着性分析在支持向量机中识别重要特征是有效的。

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《Second International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents , Dec 13-15, 2000, Hong kong, China》|2000年|p.268-273|共6页
会议地点 Hong Kong(CN);Hong Kong(CN)
作者
L. J. CAO; FRANCIS E. H. TAY;
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作者单位

Department of Mechanical Production Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore;

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原文格式 PDF
正文语种 eng
中图分类自动化技术、计算机技术;
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3. Using Support Vector Machines in Financial Time Series Forecasting [J] . Mahmoud K. Okasha International Journal of Statistics and Applications . 2014,第1期

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4. Feature selection for support vector machines in financial time series forecasting [C] . L. J. Cao, Francis E. H. Tay Intelligent Data Engineering and Automated Learning . 2000

机译：金融时序序列预测中支持向量机的功能选择
5. Feature selection methods for support vector machines for two or more classes, with applications to the analysis of Alzheimer's disease and its onset with MRI brain image processing. [D] . Aksu, Yaman. 2010

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7. Sensitivity analysis for time lag selection to forecast seasonal time series using neural networks and support vector machines [O] . Cortez Paulo 2010

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Feature Selection for Support Vector Machines in Financial Time Series Forecasting

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