Support Vector Regression for prediction of stock trend

Xia, Yaqing; Liu, Yulong; Chen, Zhiqian

doi:10.1109/iciii.2013.6703098

Cited by 25 publications

(16 citation statements)

References 4 publications

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“…There are many related researches on stock price prediction. Support vector machines was applied to build a regression model of historical stock data and to predict the trend of stocks [1]. Particle swarm optimization algorithm is used to optimize the parameters of support vector machine, which can predict the stock value robustly [2].…”

Section: Related Workmentioning

confidence: 99%

Study on the prediction of stock price based on the associated network model of LSTM

Ding

Qin

2019

Int. J. Mach. Learn. & Cyber.

200

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Stock market has received widespread attention from investors. It has always been a hot spot for investors and investment companies to grasp the change regularity of the stock market and predict its trend. Currently, there are many methods for stock price prediction. The prediction methods can be roughly divided into two categories: statistical methods and artificial intelligence methods. Statistical methods include logistic regression model, ARCH model, etc. Artificial intelligence methods include multi-layer perceptron, convolutional neural network, naive Bayes network, back propagation network, single-layer LSTM, support vector machine, recurrent neural network, etc. But these studies predict only one single value. In order to predict multiple values in one model, it need to design a model which can handle multiple inputs and produces multiple associated output values at the same time. For this purpose, it is proposed an associated deep recurrent neural network model with multiple inputs and multiple outputs based on long short-term memory network. The associated network model can predict the opening price, the lowest price and the highest price of a stock simultaneously. The associated network model was compared with LSTM network model and deep recurrent neural network model. The experiments show that the accuracy of the associated model is superior to the other two models in predicting multiple values at the same time, and its prediction accuracy is over 95%.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Section: Related Workmentioning

confidence: 99%

Study on the prediction of stock price based on the associated network model of LSTM

Ding

Qin

2019

Int. J. Mach. Learn. & Cyber.

200

View full text Add to dashboard Cite

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“…The goal of solving a regression problem is to construct a hyperplane that is as close to as many of the data points as possible. Choosing a hyperplane with small norm is considered as a main objective, while simultaneously minimizing the sum of the distances from the data points to the hyperplane [24]. In case the of solving regression problems using SVM, SVM became known as the support vector regression (SVR) where the aim is to find a function f with parameters w and b by minimizing the following regression risk:…”

Section: Support Vector Regressionmentioning

confidence: 99%

“…In the equation 16 φ (x) : x → Ω is kernel function, mapping x into in the high dimensional space, SVR and as proposed by [24]. The −ε insensitive loss function is used as follows:…”

Section: Support Vector Regressionmentioning

confidence: 99%

Predicting Financial Time Series Data Using Hybrid Model

Alhnaity

Abbod

2016

Studies in Computational Intelligence

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Prediction of Financial time series is described as one of the most challenging tasks of time series prediction, due to its characteristics and their dynamic nature. Support vector regression (SVR), Support vector machine (SVM) and back propagation neural network (BPNN) are the most popular data mining techniques in prediction financial time series. In this paper a hybrid combination model is introduced to combine the three models and to be most beneficial of them all. Quantization factor is used in this paper for the first time to improve the single SVM and SVR prediction output. And also genetic algorithm (GA) used to determine the weights of the proposed model. FTSE100, S&P 500 and Nikkei 225 daily index closing prices are used to evaluate the proposed model performance. The proposed hybrid model numerical results shows the outperform result over all other single model, traditional simple average combiner and the traditional time series model Autoregressive (AR).

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“…Originally designed for the classification, but in recent years, support vector regression algorithm in the research also showed an excellent performance. Support Vector Regression is a machine learning tool which can build a regression model on the historical time series to predict the future trend of the stock price [6].…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Survey on Stock Prediction Using Machine Learning Approaches

Borade¹

2018

IJRASET

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Support Vector Regression for prediction of stock trend

Cited by 25 publications

References 4 publications

Study on the prediction of stock price based on the associated network model of LSTM

Study on the prediction of stock price based on the associated network model of LSTM

Predicting Financial Time Series Data Using Hybrid Model

Survey on Stock Prediction Using Machine Learning Approaches

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