Journal ArticleDOI
A multiple-kernel support vector regression approach for stock market price forecasting
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TLDR
A two-stage multiple-kernel learning algorithm by incorporating sequential minimal optimization and the gradient projection method is developed, by which advantages from different hyperparameter settings can be combined and overall system performance can be improved.Abstract:
Support vector regression has been applied to stock market forecasting problems. However, it is usually needed to tune manually the hyperparameters of the kernel functions. Multiple-kernel learning was developed to deal with this problem, by which the kernel matrix weights and Lagrange multipliers can be simultaneously derived through semidefinite programming. However, the amount of time and space required is very demanding. We develop a two-stage multiple-kernel learning algorithm by incorporating sequential minimal optimization and the gradient projection method. By this algorithm, advantages from different hyperparameter settings can be combined and overall system performance can be improved. Besides, the user need not specify the hyperparameter settings in advance, and trial-and-error for determining appropriate hyperparameter settings can then be avoided. Experimental results, obtained by running on datasets taken from Taiwan Capitalization Weighted Stock Index, show that our method performs better than other methods.read more
Citations
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Journal ArticleDOI
Deep learning networks for stock market analysis and prediction
TL;DR: A systematic analysis of the use of deep learning networks for stock market analysis and prediction using five-minute intraday data from the Korean KOSPI stock market as input data to examine the effects of three unsupervised feature extraction methods.
Journal ArticleDOI
Support vector regression with chaos-based firefly algorithm for stock market price forecasting
TL;DR: A forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price and performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).
Journal ArticleDOI
A systematic review of fundamental and technical analysis of stock market predictions
TL;DR: Support vector machine and artificial neural network were found to be the most used machine learning algorithms for stock market prediction.
Journal ArticleDOI
Stock price prediction using support vector regression on daily and up to the minute prices
TL;DR: This study uses a machine learning technique called Support Vector Regression to predict stock prices for large and small capitalisations and in three different markets, employing prices with both daily and up-to-the-minute frequencies and suggests that the SVR has predictive power, especially when using a strategy of updating the model periodically.
Journal ArticleDOI
Short-term load forecasting using a kernel-based support vector regression combination model
JinXing Che,Jianzhou Wang +1 more
TL;DR: The proposed combination model provides a new way to kernel function selection of SVR model by using a novel individual model selection algorithm and increases electric load forecasting accuracy compared to the best individual kernel-based SVR models.
References
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