scholarly journals Prediction of Stock Performance Using Deep Neural Networks

2020 ◽  
Vol 10 (22) ◽  
pp. 8142
Author(s):  
Yanlei Gu ◽  
Takuya Shibukawa ◽  
Yohei Kondo ◽  
Shintaro Nagao ◽  
Shunsuke Kamijo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Performance Prediction ◽  
Deep Neural Network ◽  
Series Data ◽  
Trading System ◽  
Stock Performance ◽  
Trading Systems ◽  
Stock Traders ◽  
Proven Method

Stock performance prediction is one of the most challenging issues in time series data analysis. Machine learning models have been widely used to predict financial time series during the past decades. Even though automatic trading systems that use Artificial Intelligence (AI) have become a commonplace topic, there are few examples that successfully leverage the proven method invented by human stock traders to build automatic trading systems. This study proposes to build an automatic trading system by integrating AI and the proven method invented by human stock traders. In this study, firstly, the knowledge and experience of the successful stock traders are extracted from their related publications. After that, a Long Short-Term Memory-based deep neural network is developed to use the human stock traders’ knowledge in the automatic trading system. In this study, four different strategies are developed for the stock performance prediction and feature selection is performed to achieve the best performance in the classification of good performance stocks. Finally, the proposed deep neural network is trained and evaluated based on the historic data of the Japanese stock market. Experimental results indicate that the proposed ranking-based stock classification considering historical volatility strategy has the best performance in the developed four strategies. This method can achieve about a 20% earning rate per year over the basis of all stocks and has a lower risk than the basis. Comparison experiments also show that the proposed method outperforms conventional methods.

scholarly journals Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network

2021 ◽  
Vol 11 (9) ◽  
pp. 3984
Author(s):  
Xinpeng Yu ◽  
Dagang Li
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Performance Prediction ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Performance ◽  
Trading Systems ◽  
Stock Price Prediction ◽  
Stock Traders

Stock performance prediction plays an important role in determining the appropriate timing of buying or selling a stock in the development of a trading system. However, precise stock price prediction is challenging because of the complexity of the internal structure of the stock price system and the diversity of external factors. Although research on forecasting stock prices has been conducted continuously, there are few examples of the successful use of stock price forecasting models to develop effective trading systems. Inspired by the process of human stock traders looking for trading opportunities, we propose a deep learning framework based on a hybrid convolutional recurrent neural network (HCRNN) to predict the important trading points (IPs) that are more likely to be followed by a significant stock price rise to capture potential high-margin opportunities. In the HCRNN model, the convolutional neural network (CNN) performs convolution on the most recent region to capture local fluctuation features, and the long short-term memory (LSTM) approach learns the long-term temporal dependencies to improve stock performance prediction. Comprehensive experiments on real stock market data prove the effectiveness of our proposed framework. Our proposed method ITPP-HCRNN achieves an annualized return that is 278.46% more than that of the market.

scholarly journals Simulating Time-Series Data for Improved Deep Neural Network Performance

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 131248-131255 ◽  
Author(s):  
Jordan Yeomans ◽  
Simon Thwaites ◽  
William S. P. Robertson ◽  
David Booth ◽  
Brian Ng ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Performance ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Series Data

scholarly journals COMPARATIVE STUDY ON DEEP NEURAL NETWORK MODELS FOR CROP CLASSIFICATION USING TIME SERIES POLSAR AND OPTICAL DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 675-681 ◽  
Author(s):  
G. S. Phartiyal ◽  
D. Singh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Classification Task ◽  
Optical Data ◽  
Series Data ◽  
Study Region ◽  
Crop Classification

<p><strong>Abstract.</strong> Crop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task. In this study, a comparative analysis is made on the performances of various deep neural network (DNN) models for crop classification task using polarimetric synthetic aperture radar (PolSAR) and optical satellite data. For PolSAR data, Sentinel 1 dual pol SAR data is used. Sentinel 2 multispectral data is used as optical data. Five land cover classes including two crop classes of the season are taken. Time series data over the period of one crop cycle is used. Training and testing samples are measured and collected directly from the ground over the study region. Various convolutional neural network (CNN) and long short-term memory (LSTM) models are implemented, analysed, evaluated, and compared. Models are evaluated on the basis of classification accuracy and generalization performance.</p>

scholarly journals RAIM-NET: A Deep Neural Network for Receiver Autonomous Integrity Monitoring

2020 ◽  
Vol 12 (9) ◽  
pp. 1503
Author(s):  
Yuan Sun
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Detection ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Main Idea ◽  
Series Data ◽  
Model Parameters ◽  
Integrity Monitoring ◽  
Receiver Autonomous Integrity Monitoring

With the continuous popularization of Global Navigation Satellite System (GNSS) in various applications, the performance requirement for integrity is also increasing, especially in the field of safety-of-life. Although the existing Receiver Autonomous Integrity Monitoring (RAIM) algorithm has been embedded in the GNSS receiver as a standard method, it might still suffer from small fault detection and delay alarm problem for time series fault models. In an effort to solve this problem, a Deep Neural Network (DNN) for RAIM, named RAIM-NET, is investigated in this paper. The main idea of RAIM-NET is to propose a combination of feature vector extraction and DNN model to improve the performance of integrity monitoring, with a problem specifically designed for loss function, obtaining the model parameters. Inspired by the powerful advantages of Recurrent Neural Network (RNN) in time series data processing, a multilayer RNN is applied to build the DNN model structure and improve the detection rate for small faults and reduce the alarm delay for the time series fault event. Finally, real GNSS data experiments are designed to verify the performance of RAIM-NET in fault detection and time delay for integrity monitoring.

scholarly journals Deep anomaly detection for industrial systems: a case study

2020 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Feng Xue ◽  
Weizhong Yan ◽  
Tianyi Wang ◽  
Hao Huang ◽  
Bojun Feng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Normal Operation ◽  
Series Data ◽  
Support Vector ◽  
Industrial Systems

We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation is used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are categorical in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Tianle Zhou ◽  
Chaoyi Chu ◽  
Chaobin Xu ◽  
Weihao Liu ◽  
Hao Yu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Financial Market ◽  
Financial Time Series ◽  
Influential Factors ◽  
Series Data ◽  
Phase Point ◽  
Price Fluctuations ◽  
Financial Time

In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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