Novel Deep Learning Model with CNN and Bi-Directional LSTM for Improved Stock Market Index Prediction

Eapen, Jithin; Bein, Doina; Verma, Abhishek

doi:10.1109/ccwc.2019.8666592

Cited by 119 publications

(49 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We use the above three indicators to evaluate the effectiveness of CNN-LSTM model. The calculation formulas of the three indicators are shown in equations (12) to (14).…”

Section: B Evaluation Metricsmentioning

confidence: 99%

A Hybrid CNN-LSTM Model for Aircraft 4D Trajectory Prediction

Tian

2020

IEEE Access

109

View full text Add to dashboard Cite

The 4D trajectory is a multi-dimensional time series with plentiful spatial-temporal features and has a high degree of complexity and uncertainty. Aiming at these features of aircraft flight trajectory and the problem that it is difficult for existing trajectory prediction methods to extract spatial-temporal features from the trajectory data at the same time, we propose a novel 4D trajectory prediction hybrid architecture based on deep learning, which combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). An 1D convolution is used to extract the spatial dimension feature of the trajectory, and LSTM is used to mine the temporal dimension feature of the trajectory. Hence the highprecision prediction of the 4D trajectory is realized based on the sufficient fusion of the above features. We use real Automatic Dependent Surveillance-Broadcast (ADS-B) historical trajectory data for experiments and compare the proposed method with a single LSTM model and BP model on the same data set. The experimental results show that the trajectory prediction accuracy of the CNN-LSTM hybrid model is superior to a single model. The prediction error is reduced by an average of 21.62% compared to the LSTM model and by an average of 52.45%compared to the BP model. It provides a certain reference for the trajectory prediction research and Air Traffic Management decision-making. INDEX TERMS 4D trajectory prediction, deep learning, CNN-LSTM model, spatial-temporal feature Ma Lan (1966-) Female; Born in Huocheng, Xinjiag Province. Professor. Her main research direction is Air Traffic Information Process, Air Traffic Management Optimization. Shan Tian, born in 1996. Her research interests include data mining and deep learning with recent applications on aircraft 4D trajectory prediction.

show abstract

“…We use the above three indicators to evaluate the effectiveness of CNN-LSTM model. The calculation formulas of the three indicators are shown in equations (12) to (14).…”

Section: B Evaluation Metricsmentioning

confidence: 99%

A Hybrid CNN-LSTM Model for Aircraft 4D Trajectory Prediction

Tian

2020

IEEE Access

109

View full text Add to dashboard Cite

show abstract

“…CNN [8], RNN [10], and LSTM [11] were commonly used deep learning models in predicting the stock price movement. In addition, constructing hybrid models is a popular way to enhance the performance of model, such as SVM-ANN model [12], CNN-SVM model [13], and CNN-LSTM model [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, we proposed a CNN-LSTM model for stock price movement forecast. Compared with other CNN-LSTM models [14][15][16][17][18][19][20], the main difference between them and our proposed hybrid model lies on the CNN-based feature extraction module. eir feature extraction modules mainly aimed at extracting features from one-dimensional or two-dimensional input variables, while ours was aimed at three-dimensional input tensor.…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…The latest work also proposes a similar hybrid neural network architecture, integrating a convolutional neural network with a bidirectional long short-term memory to predict the stock market index [4]. While the researchers frequently proposed different neural network solution architectures, it brought further discussions about the topic if the high cost of training such models is worth the result or not.…”

mentioning

confidence: 99%

Short-term stock market price trend prediction using a comprehensive deep learning system

2020

View full text Add to dashboard Cite

Stock market is one of the major fields that investors are dedicated to, thus stock market price trend prediction is always a hot topic for researchers from both financial and technical domains. In this research, our objective is to build a state-of-art prediction model for price trend prediction, which focuses on short-term price trend prediction. As concluded by Fama in [26], financial time series prediction is known to be a notoriously difficult task due to the generally accepted, semi-strong form of market efficiency and the high level of noise. Back in 2003, Wang et al. in [44] already applied artificial neural networks on stock market price prediction and focused on volume, as a specific feature of stock market. One of the key findings by them was that the volume was not found to be effective in improving the forecasting performance on the datasets they used, which was S&P 500 and DJI. Ince and Trafalis in [15] targeted short-term forecasting and applied support vector machine (SVM) model on the stock price prediction. Their main contribution is performing a comparison between multi-layer perceptron (MLP) and SVM then found that most of the scenarios SVM outperformed MLP, while

show abstract

Novel Deep Learning Model with CNN and Bi-Directional LSTM for Improved Stock Market Index Prediction

Cited by 119 publications

References 11 publications

A Hybrid CNN-LSTM Model for Aircraft 4D Trajectory Prediction

A Hybrid CNN-LSTM Model for Aircraft 4D Trajectory Prediction

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

Short-term stock market price trend prediction using a comprehensive deep learning system

Contact Info

Product

Resources

About