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首页> 外文期刊>Journal of Computer and Communications >Short-Term Financial Time Series Forecasting Integrating Principal Component Analysis and Independent Component Analysis with Support Vector Regression
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Short-Term Financial Time Series Forecasting Integrating Principal Component Analysis and Independent Component Analysis with Support Vector Regression

机译：将主成分分析和独立成分分析与支持向量回归相结合的短期财务时间序列预测

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摘要

Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.
机译：金融时间序列预测可能对个人和机构投资者都有利。但是，财务数据中的高噪声和复杂性使这项工作极具挑战性。多年来，许多研究人员已经非常成功地使用支持向量回归（SVR）来克服这一挑战。本文提出了一种基于SVR的预测模型，该模型首先使用主成分分析（PCA）提取低维有效特征信息，然后使用独立成分分析（ICA）预处理提取的特征以使特征无效。要素中噪声的影响。根据孟加拉国达卡证券交易所（DSE）上市的来自三个不同行业的三只重要股票16年的历史数据进行了实验。针对短期预测，提前1至4天进行预测。为了进行比较，将PCA与SVR的集成（PCA-SVR），ICA与SVR的集成（ICA-SVR）和单个SVR方法进行了集成，以评估该方法的预测准确性。实验结果表明，所提出的模型（PCA-ICA-SVR）优于PCA-SVR，ICA-SVR和单一SVR方法。

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