A Feature Fusion Based Forecasting Model for Financial Time Series

By Zhiqiang Guo, Huaiqing Wang, QUAN LIU, Jie Yang. 2014

54 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research

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Citations in the corpus

A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017, background): “All of them are widely used indicators to measure whether the predicted value is similar to the actual value [2, 23, 33, 34].”
Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models (2016, background): “[22] and Taha [23] propose that feature selection techniques be used to combine different machine learning methods into a new effective learning system that assembles the best-performing and strongest features of each approach, while leaving out the defects and weak points; they argue that such a sy”