Stock Forecasting Model FS-LSTM Based on the 5G Internet of Things

Li, Hui; Hua, Jinjin; Li, Jinqiu; Li, Geng

doi:10.1155/2020/7681209

Cited by 13 publications

(9 citation statements)

References 21 publications

(21 reference statements)

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“…This section presents the performance evaluation of the proposed SASPF model over other prediction models. The GAN-FD model [15] was chosen for comparison as it achieved much better results than existing LSTM based stock forecasting methods [21,[24][25][26][27][28], as investors' sentiments are considered as a major contributing parameter. Results obtained for sentiment index (positive, negative, neutral and compound).…”

Section: Resultsmentioning

confidence: 99%

“…Correspondingly, in [15] a forecasting error loss and direction prediction accuracy method was proposed, noting that Generative Adversalial (GA) training can be used to integrate the loss parameter for acquiring the long-term outcomes. The GAN-FD used CNN and LSTM [24][25][26] for predicting more accurately stock prices. Nonetheless, it failed to get a good learning tradeoff performance of modeling both short and long-term market behavioral contexts.…”

Section: Introductionmentioning

confidence: 99%

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Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model

Gurav¹,

Kotrappa²

2020

Eng. Technol. Appl. Sci. Res.

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Stock market historical information is often utilized in technical analyses for identifying and evaluating patterns that could be utilized to achieve profits in trading. Although technical analysis utilizing various measures has been proven to be helpful for forecasting and predicting price trends, its utilization in formulating trading orders and rules in an automated system is complex due to the indeterminate nature of the rules. Moreover, it is hard to define a specific combination of technical measures that identify better trading rules and points, since stocks might be affected by different external factors. Thus, it is important to incorporate investors’ sentiments in forecasting operations, considering dynamically the varying stock behavior. This paper presents a sentiment aware stock forecasting model using a Log BiLinear (LBL) model for learning short term stock market sentiment patterns, and a Recurrent Neural Network (RNN) for learning long-term stock market sentiment patterns. The Sentiment Aware Stock Price Forecasting (SASPF) model achieves a much superior performance compared to standard deep learning based stock price forecasting models.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model

Gurav¹,

Kotrappa²

2020

Eng. Technol. Appl. Sci. Res.

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“…Moews et al [58] designed a forecasting method based on Deep Feed-forward Neural Network (DFNN) and exponential smoothing. In order to reduce the training complexity and improve the prediction accuracy, Li et al [59] constructed a forecasting model by integrating Feature Selection (FS) and LSTM method. For selecting and focusing on key information of stock data, Zhao et al [60] introduced AM into RNN and proposed three predication frameworks named AT-RRR, AT-LSTM, and AT-GRU, respectively.…”

Section: Deep Learning Methods DL Technique Based Onmentioning

confidence: 99%

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Chen

Fang

Liang

et al. 2021

Scientific Programming

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Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.

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“…(3) generate the initial weights w randomly for the LSTM network. (4) for each round 1 to z do (5) for each round t � 1 to m do (6) input D * t to the proposed LSTM network and generate class label vector l. (7) compare D t l and l to update w by Adam method [49]. (8) end for (9) end for (10) evaluate accuracy using the optimal weights w for testing data K. (11) output w and accuracy.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

“…Forecasting or prediction [2] is the most interesting thing in the financial market, and many studies [3,4] deploy IoTbased services to achieve higher accuracy. Li et al [5] studied the development of IoTs and used deep learning to predict stock price trends. Yang et al [6] proposed a new data mining algorithm to study the multimedia Internet of ings and the stability of stocks.…”

Section: Introductionmentioning

confidence: 99%

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

Sun

Srivastava

et al. 2021

Mobile Information Systems

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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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Stock Forecasting Model FS-LSTM Based on the 5G Internet of Things

Cited by 13 publications

References 21 publications

Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model

Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

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

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