Stock market's price movement prediction with LSTM neural networks

Nelson, David M.; Pereira, Adriano C. M.; Oliveira, Renato Arantes de

doi:10.1109/ijcnn.2017.7966019

Cited by 540 publications

(297 citation statements)

References 8 publications

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“…This approach can only predict short time price movements. The authors of [10] [11]. The method provides only a decision of increase and decrease and does not provide the price of the stock.…”

Section: Guruprasad S H Chandramoulimentioning

confidence: 99%

CNN based Stock Market Prediction

Guruprasad¹,

Chandramouli²

2020

IJEAT

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Abstract: Indian Stock market is highly dynamic and especially after globalization stock market modeling has become even more complex due to influence of multiple parameters. In presence of multiple parameters, some parameters have increased influence than others in prediction of stock market trends. This influence of individual parameters and their joint influence over time is better modeled with Convolutional Neural Network Classifiers. This work models the dynamics of stock market in terms of Convolutional Neural Networks and multiple parametersimpacting the stock trend. The proposed solution is implemented for Indian stock market for stocks in different sectors to prove its prediction accuracy.

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Section: Guruprasad S H Chandramoulimentioning

confidence: 99%

CNN based Stock Market Prediction

Guruprasad¹,

Chandramouli²

2020

IJEAT

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show abstract

“…Optimal weights are introduced for optimization and use of genetic algorithms better the prediction. Use of LSTM for stock market anticipation based on technical analysis indicators is explored in [11]. The method provides only a decision of increase and decrease and does not provide the price of the stock.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Deep Learning Based Stock Market Prediction with both Sentiment and Historic Trend Data

S*,

Mr. Sahilverma,

2020

IJITEE

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Stock market is highly volatile and it is necessary for investors to have an accurate prediction of stock prices for a better profitability. Towards this need many methods have been proposed for stock market prediction with aim to provide a higher prediction accuracy. Current methods for stock market prediction are in two categories of machine learning and statistics based. Considering the need for accurate prediction in short term and long term, the merits of both methods must be combined for accurate prediction. This work proposes a hybrid deep learning approach for stock market prediction which combines the historic price-based trend forecasting along with stock market sentiments expressed in twitter to predict the stock price trend.

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“…Nevertheless, simple RNN has long-term dependence problems and cannot effectively utilize long-interval historical information. Therefore, long short-term memory (LSTM) network has emerged to unravel the problem of gradient disappearance, which has been used for stock price forecasting [40], air quality forecasting, sea surface temperature forecasting [41], flight passenger number forecasting, and speech recognition [42]. The results illustrated that the model had achieved excellent performance.…”

Section: Introductionmentioning

confidence: 99%

Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

2020

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Noise pollution is one of the major urban environmental pollutions, and it is increasingly becoming a matter of crucial public concern. Monitoring and predicting environmental noise are of great significance for the prevention and control of noise pollution. With the advent of the Internet of Things (IoT) technology, urban noise monitoring is emerging in the direction of a small interval, long time, and large data amount, which is difficult to model and predict with traditional methods. In this study, an IoT-based noise monitoring system was deployed to acquire the environmental noise data, and a two-layer long short-term memory (LSTM) network was proposed for the prediction of environmental noise under the condition of large data volume. The optimal hyperparameters were selected through testing, and the raw data sets were processed. The urban environmental noise was predicted at time intervals of 1 s, 1 min, 10 min, and 30 min, and their performances were compared with three classic predictive models: random walk (RW), stacked autoencoder (SAE), and support vector machine (SVM). The proposed model outperforms the other three existing classic methods. The time interval of the data set has a close connection with the performance of all models. The results revealed that the LSTM network could reflect changes in noise levels within one day and has good prediction accuracy. Impacts of monitoring point location on prediction results and recommendations for environmental noise management were also discussed in this paper.

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Stock market's price movement prediction with LSTM neural networks

Cited by 540 publications

References 8 publications

CNN based Stock Market Prediction

CNN based Stock Market Prediction

Hybrid Deep Learning Based Stock Market Prediction with both Sentiment and Historic Trend Data

Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

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