TRNN: An efficient time-series recurrent neural network for stock price prediction

Lu, Minrong; Xu, Xuerong

doi:10.1016/j.ins.2023.119951

Cited by 19 publications

(2 citation statements)

References 34 publications

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“…b h and b y are bias terms, and σ denotes the activation function, typically a sigmoid or tanh function. Despite RNNs' potential for sequential data processing, they face challenges with gradient vanishing or exploding when dealing with long sequences in practical applications [27], limiting their capability to learn long-term dependencies. To overcome this limitation, LSTMs were introduced [28].…”

Section: Rnn and Lstmmentioning

confidence: 99%

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Huo,

Guo,

Yang

et al. 2024

Systems

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In this research, an innovative state space-based Transformer model is proposed to address the challenges of complex system prediction tasks. By integrating state space theory, the model aims to enhance the capability to capture dynamic changes in complex data, thereby improving the accuracy and robustness of prediction tasks. Extensive experimental validations were conducted on three representative tasks, including legal case judgment, legal case translation, and financial data analysis to assess the performance and application potential of the model. The experimental results demonstrate significant performance improvements of the proposed model over traditional Transformer models and other advanced variants such as Bidirectional Encoder Representation from Transformers (BERT) and Finsformer across all evaluated tasks. Specifically, in the task of legal case judgment, the proposed model exhibited a precision of 0.93, a recall of 0.90, and an accuracy of 0.91, significantly surpassing the traditional Transformer model (with precision of 0.78, recall of 0.73, accuracy of 0.76) and performances of other comparative models. In the task of legal case translation, the precision of the proposed model reached 0.95, with a recall of 0.91 and an accuracy of 0.93, also outperforming other models. Likewise, in the task of financial data analysis, the proposed model also demonstrated excellent performance, with a precision of 0.94, recall of 0.90, and accuracy of 0.92. The state space-based Transformer model proposed not only theoretically expands the research boundaries of deep learning models in complex system prediction but also validates its efficiency and broad application prospects through experiments. These achievements provide new insights and directions for future research and development of deep learning models, especially in tasks requiring the understanding and prediction of complex system dynamics.

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Section: Rnn and Lstmmentioning

confidence: 99%

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Huo,

Guo,

Yang

et al. 2024

Systems

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show abstract

“…Because the LSTM neural network is a time series model, how different sliding windows are configured affects how accurate the prediction results are [10]. The prediction results have a considerable variation and some data fluctuations when the sliding window is 5, as Table 1 illustrates.…”

Section: Sliding Window Settingsmentioning

confidence: 99%

Research on Stock Price Prediction Model Based on LSTM

Nie

2024

HSET

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Stock prices are a non-linear, long-term series of data. The research topic of this paper is to use the Long Short-Term Memory Network (LSTM) model to analyze the historical stock price data of BYD (BYD) to predict the future stock price trend, and to provide intelligent reference for investors by evaluating the model performance, aiming to improve the accuracy of stock price prediction. The accuracy and consistency of stock price prediction can be increased by using this method, which successfully takes advantage of the time characteristics in historical data. After explaining the idea and architecture of LSTM, this article creates a stock price prediction model based on it, using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as evaluation metrics. At the same time, network structures with different layers were designed to verify the effectiveness of the model, and the experimental results were shown. The advantages and limitations of the model are highlighted, and the feasibility of deep learning in the stock financial market and the credibility and advantages of strategic decision-making are demonstrated.

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Time series forecasting of stock market indices based on DLWR-LSTM model

Yao,

Yan

2024

Finance Research Letters

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TRNN: An efficient time-series recurrent neural network for stock price prediction

Cited by 19 publications

References 34 publications

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Research on Stock Price Prediction Model Based on LSTM

Time series forecasting of stock market indices based on DLWR-LSTM model

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