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A Feature Fusion Based Forecasting Model for Financial Time Series

机译：基于特征融合的金融时间序列预测模型

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摘要

Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models.
机译：预测股票市场已成为研究人员和投资者越来越感兴趣的研究领域，并且已经提出了许多预测模型。在这些模型中，特征选择技术用于预处理原始数据并消除噪声。本文构建了一个预测模型，借助独立成分分析，规范相关分析和支持向量机来预测股票市场的行为。首先，从历史收盘价和通过独立成分分析获得的39个技术变量中提取出两种类型的特征。其次，采用规范的相关分析方法来组合两种类型的特征并提取固有特征，以提高预测模型的性能。最后，使用支持向量机预测第二天的收盘价。将该模型应用于上海股市指数和道琼斯指数，实验结果表明，该模型在预测范围内比其他两个相似模型表现更好。

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