scholarly journals Development of an Information and Analytical System for Predicting Price Fluctuations and Long-Term Trends Using Generative-Adversarial Neural Networks

2021 ◽  
Vol 110 ◽  
pp. 05010
Author(s):  
Lyudmila Filippova ◽  
Anna Sazonova ◽  
Yuriy Leonov ◽  
Polina Shatova
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Short Term Memory ◽  
Financial Assets ◽  
Stock Market Forecasting ◽  
Long Term Trends ◽  
Multi Level ◽  
Russian Stock Market ◽  
Analytical System

Deep learning methods (DML) have been widely used in financial fields recently, such as stock market forecasting, balance the portfolio, financial information processing, and transaction execution strategies. Stock market forecasting and effective trading strategy construction, when using deep learning, are the most popular ways of applying DML in the field of finance. Against the background of the general development of the Russian stock market, the study and investigation of its price dynamics is a highly promising direction for analyzing and forecasting the value of financial assets in which it is planned to invest money. In this study, a new architecture of a conditional generative-adversarial neural network (GAN) with a multi-level perceptron (MLP) as a discriminator and a long short-term memory (LSTM) as a generator for determining trends is proposed. The Box-Jenkins method (ARIMA) is used to determine the confidence interval.

scholarly journals Stock Price Forecasting with Deep Learning: A Comparative Study

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1441
Author(s):  
Tej Bahadur Shahi ◽  
Ashish Shrestha ◽  
Arjun Neupane ◽  
William Guo
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Comparative Study ◽  
Stock Price ◽  
Short Term Memory ◽  
Price Forecasting ◽  
Stock Price Forecasting ◽  
Financial News ◽  
Stock Market Forecasting ◽  
News Sentiment

The long short-term memory (LSTM) and gated recurrent unit (GRU) models are popular deep-learning architectures for stock market forecasting. Various studies have speculated that incorporating financial news sentiment in forecasting could produce a better performance than using stock features alone. This study carried a normalized comparison on the performances of LSTM and GRU for stock market forecasting under the same conditions and objectively assessed the significance of incorporating the financial news sentiments in stock market forecasting. This comparative study is conducted on the cooperative deep-learning architecture proposed by us. Our experiments show that: (1) both LSTM and GRU are circumstantial in stock forecasting if only the stock market features are used; (2) the performance of LSTM and GRU for stock price forecasting can be significantly improved by incorporating the financial news sentiments with the stock features as the input; (3) both the LSTM-News and GRU-News models are able to produce better forecasting in stock price equally; (4) the cooperative deep-learning architecture proposed in this study could be modified as an expert system incorporating both the LSTM-News and GRU-News models to recommend the best possible forecasting whichever model can produce dynamically.

scholarly journals Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 13099-13111
Author(s):  
Khaled A. Althelaya ◽  
Salahadin A. Mohammed ◽  
El-Sayed M. El-Alfy
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Multiresolution Analysis ◽  
Stock Market Forecasting

scholarly journals Demand Management of Station-Based Car Sharing System Based on Deep Learning Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Daben Yu ◽  
Zongping Li ◽  
Qinglun Zhong ◽  
Yi Ai ◽  
Wei Chen
Keyword(s):  
Deep Learning ◽  
Information Technologies ◽  
Short Term Memory ◽  
Demand Management ◽  
Short Term ◽  
Car Sharing ◽  
Real World Data ◽  
Temporal Perspective ◽  
Analytical System ◽  
Operation Status

Metropolitan development has motivated car sharing into an attractive type of car leasing with the help of information technologies. In this paper, we propose a new approach based on deep learning techniques to assess the operation of a station-based car sharing system. First, we analyse the pick-up and drop-off operations of the station-based car sharing system, capturing the operational features of car sharing service and the behaviours of vehicle use from a temporal perspective. Then, we introduced an analytical system to detect the system operation concerning the spontaneous deviations derived from user demands from service provisions. We employed Long Short-Term Memory (LSTM) structure to forecast short-term future vehicle uses. An experimental case based on real-world data is reported to demonstrate the effectiveness of this approach. The results prove that the proposed structure generates high-quality predictions and the operation status derived from user demands.

scholarly journals A Novel Stock Index Intelligent Prediction Algorithm Based on Attention-Guided Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yangzi Zhao
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Stock Index ◽  
Prediction Algorithm ◽  
Short Term ◽  
Term Memory ◽  
Model Based

The stock market is affected by economic market, policy, and other factors, and its internal change law is extremely complex. With the rapid development of the stock market and the expansion of the scale of investors, the stock market has produced a large number of transaction data, which makes it more difficult to obtain valuable information. Because deep neural network is good at dealing with the prediction problems with large amount of data and complex nonlinear mapping relationship, this paper proposes an attention-guided deep neural network stock prediction algorithm. This paper synthesizes the daily stock social media text emotion index and stock technology index as the data source and applies them to the long-term and short-term memory neural network (LSTM) model to predict the stock market. The stock emotion index is extracted by constructing a social text classification emotion model of bidirectional long-term and short-term memory neural network (Bi-LSTM) based on attention mechanism and glove word vector representation algorithm. In addition, a dimensionality reduction model based on decision tree (DT) and principal component analysis (PCA) is constructed to reduce the dimensionality of stock technical indicators and extract the main data information. Furthermore, this paper proposes a model based on nasNet for pattern recognition. The recognition results can be used to automatically identify short-term K-line patterns, predict reliable trading signals, and help investors customize short-term high-efficiency investment strategies. The experimental results show that the prediction accuracy of the proposed algorithm can reach 98.6%, which has high application value.

scholarly journals Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11262
Author(s):  
Guobin Li ◽  
Xiuquan Du ◽  
Xinlu Li ◽  
Le Zou ◽  
Guanhong Zhang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Dna Binding ◽  
Binding Proteins ◽  
Prediction Models ◽  
Short Term Memory ◽  
Local Features ◽  
Dna Binding Proteins ◽  
Superior Performance

DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.

scholarly journals Real Time LDR Data Prediction using IoT and Deep Learning Algorithm

2020 ◽  
pp. 158-161
Author(s):  
Chandraprabha S ◽  
Pradeepkumar G ◽  
Dineshkumar Ponnusamy ◽  
Saranya M D ◽  
Satheesh Kumar S ◽  
...  
Keyword(s):  
Deep Learning ◽  
Light Intensity ◽  
Real Time ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Time Interval ◽  
Deep Learning Algorithm ◽  
Data Prediction ◽  
Solar Plant

This paper outfits artificial intelligence based real time LDR data which is implemented in various applications like indoor lightning, and places where enormous amount of heat is produced, agriculture to increase the crop yield, Solar plant for solar irradiance Tracking. For forecasting the LDR information. The system uses a sensor that can measure the light intensity by means of LDR. The data acquired from sensors are posted in an Adafruit cloud for every two seconds time interval using Node MCU ESP8266 module. The data is also presented on adafruit dashboard for observing sensor variables. A Long short-term memory is used for setting up the deep learning. LSTM module uses the recorded historical data from adafruit cloud which is paired with Node MCU in order to obtain the real-time long-term time series sensor variables that is measured in terms of light intensity. Data is extracted from the cloud for processing the data analytics later the deep learning model is implemented in order to predict future light intensity values.

Attention-Based Deep Recurrent Model for Survival Prediction

2021 ◽  
Vol 2 (4) ◽  
pp. 1-18
Author(s):  
Zhaohong Sun ◽  
Wei Dong ◽  
Jinlong Shi ◽  
Kunlun He ◽  
Zhengxing Huang
Keyword(s):  
Risk Factors ◽  
Survival Analysis ◽  
Deep Learning ◽  
Service Management ◽  
Short Term Memory ◽  
Survival Prediction ◽  
Learning Techniques ◽  
Proposed Model ◽  
Traditional Approaches

Survival analysis exhibits profound effects on health service management. Traditional approaches for survival analysis have a pre-assumption on the time-to-event probability distribution and seldom consider sequential visits of patients on medical facilities. Although recent studies leverage the merits of deep learning techniques to capture non-linear features and long-term dependencies within multiple visits for survival analysis, the lack of interpretability prevents deep learning models from being applied to clinical practice. To address this challenge, this article proposes a novel attention-based deep recurrent model, named AttenSurv , for clinical survival analysis. Specifically, a global attention mechanism is proposed to extract essential/critical risk factors for interpretability improvement. Thereafter, Bi-directional Long Short-Term Memory is employed to capture the long-term dependency on data from a series of visits of patients. To further improve both the prediction performance and the interpretability of the proposed model, we propose another model, named GNNAttenSurv , by incorporating a graph neural network into AttenSurv, to extract the latent correlations between risk factors. We validated our solution on three public follow-up datasets and two electronic health record datasets. The results demonstrated that our proposed models yielded consistent improvement compared to the state-of-the-art baselines on survival analysis.

scholarly journals Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning

Telecom ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 446-471
Author(s):  
Percy Kapadia ◽  
Boon-Chong Seet
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Access Network ◽  
Edge Computing ◽  
Fifth Generation ◽  
Radio Access ◽  
Ping Pong ◽  
Lstm Network ◽  
Multi Access

This paper proposes a potential enhancement of handover for the next-generation multi-tier cellular network, utilizing two fifth-generation (5G) enabling technologies: multi-access edge computing (MEC) and machine learning (ML). MEC and ML techniques are the primary enablers for enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC). The subset of ML chosen for this research is deep learning (DL), as it is adept at learning long-term dependencies. A variant of artificial neural networks called a long short-term memory (LSTM) network is used in conjunction with a look-up table (LUT) as part of the proposed solution. Subsequently, edge computing virtualization methods are utilized to reduce handover latency and increase the overall throughput of the network. A realistic simulation of the proposed solution in a multi-tier 5G radio access network (RAN) showed a 40–60% improvement in overall throughput. Although the proposed scheme may increase the number of handovers, it is effective in reducing the handover failure (HOF) and ping-pong rates by 30% and 86%, respectively, compared to the current 3GPP scheme.

scholarly journals An Analysis of Emotional Speech Recognition for Tamil Language Using Deep Learning Gate Recurrent Unit

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Bennilo Fernandes ◽  
Kasiprasad Mannepalli
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Short Term Memory ◽  
Estimation Method ◽  
Emotional Speech ◽  
New Approach ◽  
Training Time ◽  
Emotional Speech Recognition ◽  
Gated Recurrent Unit

Designing the interaction among human language and a registered emotional database enables us to explore how the system performs and has multiple approaches for emotion detection in patient services. As of now, clustering techniques were primarily used in many prominent areas and in emotional speech recognition, even though it shows best results a new approach to the design is focused on Long Short-Term Memory (LSTM), Bi-Directional LSTM and Gated Recurrent Unit (GRU) as an estimation method for emotional Tamil datasets is available in this paper. A new approach of Deep Hierarchal LSTM/BiLSTM/GRU layer is designed to obtain the best result for long term learning voice dataset. Different combinations of deep learning hierarchal architecture like LSTM & GRU (DHLG), BiLSTM & GRU (DHBG), GRU & LSTM (DHGL), GRU & BiLSTM (DHGB) and dual GRU (DHGG) layer is designed with introduction of dropout layer to overcome the learning problem and gradient vanishing issues in emotional speech recognition. Moreover, to increase the design outcome within each emotional speech signal, various feature extraction combinations are utilized. From the analysis an average classification validity of the proposed DHGB model gives 82.86%, which is slightly higher than other models like DHGL (82.58), DHBG (82%), DHLG (81.14%) and DHGG (80%). Thus, by comparing all the models DHGB gives prominent outcome of 5% more than other four models with minimum training time and low dataset.

Sign in / Sign up

Export Citation Format

Share Document