An Adaptive SVR for High-Frequency Stock Price Forecasting

Guo, Yanhui; Han, Shihui; Shen, Chuanhe; Liu, Ying; Yin, Xijie; Bai, Yu

doi:10.1109/access.2018.2806180

Cited by 82 publications

(54 citation statements)

References 17 publications

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“…And the results showed that the proposed ANN model is found as a dominant model compared with the other models. Besides, Yin and Bai [27] designed an adaptive SVR for stock data at different time scales. Experimental results showed that the improved SVR with dynamic optimization of learning parameters by PSO can achieve a better result than the traditional SVR.…”

Section: Model Enhancementmentioning

confidence: 99%

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Yang

Zhai

Tao

2020

Mathematical Problems in Engineering

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The prediction of stock price movement direction is significant in financial studies. In recent years, a number of deep learning models have gradually been applied for stock predictions. This paper presents a deep learning framework to predict price movement direction based on historical information in financial time series. The framework combines a convolutional neural network (CNN) for feature extraction and a long short-term memory (LSTM) network for prediction. We specifically use a three-dimensional CNN for data input in the framework, including the information on time series, technical indicators, and the correlation between stock indices. And in the three-dimensional input tensor, the technical indicators are converted into deterministic trend signals and the stock indices are ranked by Pearson product-moment correlation coefficient (PPMCC). When training, a fully connected network is used to drive the CNN to learn a feature vector, which acts as the input of concatenated LSTM. After both the CNN and the LSTM are trained well, they are finally used for prediction in the testing set. The experimental results demonstrate that the framework outperforms state-of-the-art models in predicting stock price movement direction.

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Section: Model Enhancementmentioning

confidence: 99%

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Yang

Zhai

Tao

2020

Mathematical Problems in Engineering

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“…Vapnik developed the support vector machine (SVM) which rooted in structural risk minimum (SRM) can obtain the global optimal solution by solving a convex optimization problem [18]. A diversity of function approximation applications are resorted to the support vector regression (SVR), which replaces the hard margin in conventional SVM with the soft margin [19], [20]. Without loss of any generation, temperature compensation task can be treated as a regression problem.…”

Section: Introductionmentioning

confidence: 99%

Temperature Compensation of Piezo-Resistive Pressure Sensor Utilizing Ensemble AMPSO-SVR Based on Improved Adaboost.RT

Zhang

et al. 2020

IEEE Access

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As the silicon material is severely influenced by the ambient temperature, the silicon piezo-resistive pressure sensor remarkably suffers from a strong nonlinearity in the response characteristic as the ambient temperature varies. To address this crucial issue, an adaptive mutation particle swarm optimization optimized support vector regression (AMPSO-SVR) combined with improved AdaBoost.RT algorithm is presented. The opposition-based learning initialization and Levy mutation is applied in the adaptive mutation particle swarm optimization (AMPSO) to achieve the appropriate model selection task which directly determines the performance of SVR. The performance of original AdaBoost.RT is improved by a dynamical modification approach for threshold and quoted error criterion. In order to verify the effectiveness of the proposed temperature compensation approach, several additional optimization methods such as Cuckoo search (CS), dragonfly algorithm (DA), multi-verse optimizer (MVO), conventional particle swarm optimization (PSO), Levy flight improved particle swarm optimization (Levy-PSO) and the AMPSO combined with SVR are investigated. The minimum quoted error, maximum quoted error, the mean quoted error and the variance of the quoted error over testing data obtained by the proposed method are 6.8764×10 −5 , 6.4463×10 −4 , 3.2619×10 −4 and 2.5714×10 −8 respectively, which are superior to the corresponding indices obtained by other methods. The analysis of simulation results indicates the method proposed in this research is applicable, effective and efficient for industrial application.

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“…Gosswami, et al [6] designed a principal component analysis based BP neural network model to predict the stock price of ten different types of companies from the Dhaka Stock Exchange in Bangladesh, and the results show that it is a feasible method. Guo, et al [7] studied stock data of three different time scales by using the Support Vector Regression (SVR) method with particle swarm optimization, and found that the improved SVR has better accuracy than traditional SVR and BP. Chen, et al [8] combined the financial domain knowledge with the filtering banking mechanism to improve the convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

Financial High-Frequency Time Series Forecasting Based on Sub-Step Grid Search Long Short-Term Memory Network

Tian

2020

IEEE Access

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An Adaptive SVR for High-Frequency Stock Price Forecasting

Cited by 82 publications

References 17 publications

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Temperature Compensation of Piezo-Resistive Pressure Sensor Utilizing Ensemble AMPSO-SVR Based on Improved Adaboost.RT

Financial High-Frequency Time Series Forecasting Based on Sub-Step Grid Search Long Short-Term Memory Network

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