A Novel Synergetic LSTM-GA Stock Trading Suggestion System in Internet of Things

The Internet of Things (IoT) play an important role in the financial sector in recent decades since several stock prediction models can be performed accurately according to IoT-based services. In real-time applications, the accuracy of the stock price fluctuation forecast is very important to investors, and it helps investors better manage their funds when formulating trading strategies. It has always been a goal and difficult problem for financial researchers to use predictive tools to obtain predicted values closer to actual values from a given financial data set. Leading indicators such as futures and options can reflect changes in many markets, such as the industry’s prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. In this research, a trading strategy for finding stock trading signals is proposed that combines long short-term memory neural networks with genetic algorithms. This new framework is called long short-term memory neural network with leading index, or LSTMLI for short. We thus take the stock markets of the United States and Taiwan as the research objects and use historical data, futures, and options as data sets to predict the stock prices of these two markets. After that, we use genetic algorithms to find trading signals for the designed stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.

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A Dense Long Short-Term Memory Model for Enhancing the Imagery-Based Brain-Computer Interface

Computational Intelligence and Neuroscience ◽

10.1155/2021/6614677 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Xiaofei Zhang ◽

Tao Wang ◽

Qi Xiong ◽

Yina Guo

Keyword(s):

Short Term Memory ◽

Recognition Algorithm ◽

Training Process ◽

Short Term ◽

Data Set ◽

Static State ◽

Term Memory ◽

Long Short Term Memory ◽

Model Training ◽

The One

Imagery-based brain-computer interfaces (BCIs) aim to decode different neural activities into control signals by identifying and classifying various natural commands from electroencephalogram (EEG) patterns and then control corresponding equipment. However, several traditional BCI recognition algorithms have the “one person, one model” issue, where the convergence of the recognition model’s training process is complicated. In this study, a new BCI model with a Dense long short-term memory (Dense-LSTM) algorithm is proposed, which combines the event-related desynchronization (ERD) and the event-related synchronization (ERS) of the imagery-based BCI; model training and testing were conducted with its own data set. Furthermore, a new experimental platform was built to decode the neural activity of different subjects in a static state. Experimental evaluation of the proposed recognition algorithm presents an accuracy of 91.56%, which resolves the “one person one model” issue along with the difficulty of convergence in the training process.

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An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Frontiers in Water ◽

10.3389/frwa.2021.723548 ◽

2021 ◽

Vol 3 ◽

Author(s):

Yueling Ma ◽

Carsten Montzka ◽

Bagher Bayat ◽

Stefan Kollet

Keyword(s):

Soil Moisture ◽

Indirect Method ◽

Short Term Memory ◽

Groundwater Table ◽

Short Term ◽

Data Set ◽

Term Memory ◽

Continental Scale ◽

Groundwater Table Depth ◽

Long Short Term Memory

The lack of high-quality continental-scale groundwater table depth observations necessitates developing an indirect method to produce reliable estimation for water table depth anomalies (wtda) over Europe to facilitate European groundwater management under drought conditions. Long Short-Term Memory (LSTM) networks are a deep learning technology to exploit long-short-term dependencies in the input-output relationship, which have been observed in the response of groundwater dynamics to atmospheric and land surface processes. Here, we introduced different input variables including precipitation anomalies (pra), which is the most common proxy of wtda, for the networks to arrive at improved wtda estimates at individual pixels over Europe in various experiments. All input and target data involved in this study were obtained from the simulated TSMP-G2A data set. We performed wavelet coherence analysis to gain a comprehensive understanding of the contributions of different input variable combinations to wtda estimates. Based on the different experiments, we derived an indirect method utilizing LSTM networks with pra and soil moisture anomaly (θa) as input, which achieved the optimal network performance. The regional medians of test R2 scores and RMSEs obtained by the method in the areas with wtd ≤ 3.0 m were 76–95% and 0.17–0.30, respectively, constituting a 20–66% increase in median R2 and a 0.19–0.30 decrease in median RMSEs compared to the LSTM networks only with pra as input. Our results show that introducing θa significantly improved the performance of the trained networks to predict wtda, indicating the substantial contribution of θa to explain groundwater anomalies. Also, the European wtda map reproduced by the method had good agreement with that derived from the TSMP-G2A data set with respect to drought severity, successfully detecting ~41% of strong drought events (wtda ≥ 1.5) and ~29% of extreme drought events (wtda ≥ 2) in August 2015. The study emphasizes the importance to combine soil moisture information with precipitation information in quantifying or predicting groundwater anomalies. In the future, the indirect method derived in this study can be transferred to real-time monitoring of groundwater drought at the continental scale using remotely sensed soil moisture and precipitation observations or respective information from weather prediction models.

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PrivItem2Vec: A privacy-preserving algorithm for top-N recommendation

International Journal of Distributed Sensor Networks ◽

10.1177/15501477211061250 ◽

2021 ◽

Vol 17 (12) ◽

pp. 155014772110612

Author(s):

Zhengqiang Ge ◽

Xinyu Liu ◽

Qiang Li ◽

Yu Li ◽

Dong Guo

Keyword(s):

Time Series ◽

Internet Of Things ◽

Privacy Protection ◽

Short Term Memory ◽

Short Term ◽

Recommendation Algorithm ◽

Data Set ◽

Term Memory ◽

Protection Mechanism ◽

Long Short Term Memory

To significantly protect the user’s privacy and prevent the user’s preference disclosure from leading to malicious entrapment, we present a combination of the recommendation algorithm and the privacy protection mechanism. In this article, we present a privacy recommendation algorithm, PrivItem2Vec, and the concept of the recommended-internet of things, which is a privacy recommendation algorithm, consisting of user’s information, devices, and items. Recommended-internet of things uses bidirectional long short-term memory, based on item2vec, which improves algorithm time series and the recommended accuracy. In addition, we reconstructed the data set in conjunction with the Paillier algorithm. The data on the server are encrypted and embedded, which reduces the readability of the data and ensures the data’s security to a certain extent. Experiments show that our algorithm is superior to other works in terms of recommended accuracy and efficiency.

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Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

Complexity ◽

10.1155/2020/1846926 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Daniel Štifanić ◽

Jelena Musulin ◽

Adrijana Miočević ◽

Sandi Baressi Šegota ◽

Roman Šubić ◽

...

Keyword(s):

Crude Oil ◽

Stock Prices ◽

Short Term Memory ◽

Oil Price ◽

Stationary Wavelet Transform ◽

Crude Oil Price ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

The Impact

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.

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Stock Prediction Using Sentiment Analysis and LSTM

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38231 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1752-1756

Author(s):

Ms. Anjima K. S

Keyword(s):

Stock Market ◽

Sentiment Analysis ◽

Programming Language ◽

Stock Prices ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Python Programming Language ◽

Long Short Term Memory ◽

Python Programming

Abstract: The stock market is a difficult area to anticipate since it is influenced by a variety of variables at the same time. The stock exchange is where equities are exchanged, transferred, and circulated. This research proposes a hybrid algorithm that predicts a stock's next day closing prices using sentiment analysis and Long Short Term Memory. The LSTM model seems to be quite popular in time-series forecasting, which is why it was selected for this project. Our proposed methodology makes use of the temporal association between public opinion and stock prices. Part-of-speech tagging is used to do sentiment analysis, and Long Short Term Memory is utilized to predict the stock's next day closing price. When these two factors are combined, we get a good picture of the stock's future. In this project, two main datasets have been used: HCLTECH company stock data and the news related to each stock of the HCL company for each day. The project is implemented by using the python programming language. The python programming language has been used to execute the project. This also incorporates machine learning along with public feedback. Sentiment analysis enables us to evaluate a diversity of political and economic factors, which have a significant impact on the stock market. Keywords: LSTM, sentiment analysis, RNN, Back propagation neural network.

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HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/324 ◽

2018 ◽

Cited By ~ 24

Author(s):

Dejiang Kong ◽

Fei Wu

Keyword(s):

Real Time ◽

Short Term Memory ◽

Location Based Services ◽

Location Prediction ◽

Trajectory Data ◽

Short Term ◽

Data Set ◽

Term Memory ◽

Long Time ◽

Long Short Term Memory

The widely use of positioning technology has made mining the movements of people feasible and plenty of trajectory data have been accumulated. How to efficiently leverage these data for location prediction has become an increasingly popular research topic as it is fundamental to location-based services (LBS). The existing methods often focus either on long time (days or months) visit prediction (i.e., the recommendation of point of interest) or on real time location prediction (i.e., trajectory prediction). In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users' movement in next minutes or hours. We propose a Spatial-Temporal Long-Short Term Memory (ST-LSTM) model which naturally combines spatial-temporal influence into LSTM to mitigate the problem of data sparsity. Further, we employ a hierarchical extension of the proposed ST-LSTM (HST-LSTM) in an encoder-decoder manner which models the contextual historic visit information in order to boost the prediction performance. The proposed HST-LSTM is evaluated on a real world trajectory data set and the experimental results demonstrate the effectiveness of the proposed model.

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Implementasi Long Short-Term Memory Pada Harga Saham Perusahaan Perkebunan Di Indonesia

Unisda Journal of Mathematics and Computer Science (UJMC) ◽

10.52166/ujmc.v6i01.1927 ◽

2020 ◽

Vol 6 (01) ◽

pp. 9-18

Author(s):

Rahmadi Yotenka ◽

Fazano Fikri El Huda

Keyword(s):

Stock Prices ◽

Short Term Memory ◽

Commodity Price ◽

Share Price ◽

Short Term ◽

Term Memory ◽

Price Fluctuations ◽

Climate Forecasting ◽

Long Short Term Memory ◽

Hidden Neurons

The decline and increase in the price of shares of plantation companies is a problem for investors in making decisions to buy or sell shares. Factors influencing the movement of plantation stock prices include CPO commodity price fluctuations, world oil price fluctuations, Rupiah exchange rate fluctuations, government regulations and policies, demands from importing countries, and climate. Forecasting stock prices is expected to help investors to deal with uncertainty in the movement of plantation stock prices. This study applies the Long Short-Term Memory (LSTM) to predict the stock prices of plantation companies using SSMS, LSIP, and SIMP share price data from the period 1 July 2014 - 22 July 2019. Based on the results of the study it was found that the best LSTM model on SSMS shares by using the RMSProp optimizer and 70 hidden neurons produced an RMSE value of 21,328. Then the best LSTM model on LSIP stock by using Adam optimizer and 80 hidden neurons produces an RMSE value of 33,097. Whereas the best LSTM model on SIMP shares using Adamax optimizer and 100 hidden neurons produced an RMSE value of 8,3337.

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Predicting machine’s performance data using the stacked long short-term memory (LSTM) neural networks

10.21203/rs.3.rs-961567/v1 ◽

2021 ◽

Author(s):

Jianrong Dai

Keyword(s):

Short Term Memory ◽

Performance Data ◽

Coefficient Of Determination ◽

Short Term ◽

Data Set ◽

Term Memory ◽

Machine Performance ◽

Machine Failure ◽

The Mean ◽

Long Short Term Memory

Abstract Purpose Machine Performance Check (MPC) is a daily quality assurance (QA) tool for Varian machines. The daily QA data based on MPC tests show machine performance patterns and potentially provide warning messages for preventive actions. This study developed a neural network model that could predict the trend of data variations quantitively. Methods and materials: MPC data used were collected daily for 3 years. The stacked long short-term memory (LSTM)model was used to develop the neural work model. To compare the stacked LSTM, the autoregressive integrated moving average model (ARIMA) was developed on the same data set. Cubic interpolation was used to double the amount of data to enhance prediction accuracy. After then, the data were divided into 3 groups: 70% for training, 15% for validation, and 15% for testing. The training set and the validation set were used to train the stacked LSTM with different hyperparameters to find the optimal hyperparameter. Furthermore, a greedy coordinate descent method was employed to combinate different hyperparameter sets. The testing set was used to assess the performance of the model with the optimal hyperparameter combination. The accuracy of the model was quantified by the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2). Results A total of 867 data were collected to predict the data for the next 5 days. The mean MAE, RMSE, and \({\text{R}}^{2}\) with all MPC tests was 0.013, 0.020, and 0.853 in LSTM, while 0.021, 0.030, and 0.618 in ARIMA, respectively. The results show that the LSTM outperforms the ARIMA. Conclusions In this study, the stacked LSTM model can accurately predict the daily QA data based on MPC tests. Predicting future performance data based on MPC tests will foresee possible machine failure, allowing early machine maintenance and reducing unscheduled machine downtime.

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Implementation of Long Short-Term Memory and Gated Recurrent Units on Grouped Time-Series Data to Predict Stock Prices Accurately

10.21203/rs.3.rs-1057875/v1 ◽

2021 ◽

Author(s):

Armin Lawi ◽

Hendra Mesra ◽

Supri Amir

Keyword(s):

Time Series ◽

Stock Prices ◽

Stock Price ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Percentage Error ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Abstract Stocks are an attractive investment option since they can generate large profits compared to other businesses. The movement of stock price patterns on the stock market is very dynamic; thus it requires accurate data modeling to forecast stock prices with a low error rate. Forecasting models using Deep Learning are believed to be able to accurately predict stock price movements using time-series data, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. However, several previous implementation studies have not been able to obtain convincing accuracy results. This paper proposes the implementation of the forecasting method by classifying the movement of time-series data on company stock prices into three groups using LSTM and GRU. The accuracy of the built model is evaluated using loss functions of Rooted Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results showed that the performance evaluation of both architectures is accurate in which GRU is always superior to LSTM. The highest validation for GRU was 98.73% (RMSE) and 98.54% (MAPE), while the LSTM validation was 98.26% (RMSE) and 97.71% (MAPE).

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