A Data Organization Method for LSTM and Transformer When Predicting Chinese Banking Stock Prices

Peng, Zong-Yu; Guo, Pei-Chang

doi:10.1155/2022/7119678

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…The Informer algorithm uses the ProbSparse Self-attention mechanism in the process of encoding and decoding, and only considers the part that contributes the most to the attention mechanism. Compared with LSTM [11] and Transformer [12], the calculation amount is smaller and the memory usage is low.…”

Section: Informer Model For Long-term Stock Price Predictionmentioning

confidence: 99%

Long-term stock price forecast based on PSO-informer model

Liu

Deng²,

Wei³

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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The long-term prediction of stock prices provides an important reference for quantitative investment decisions. Aiming at the problem of insufficient accuracy of long-term series prediction in existing stock forecasting models, this paper proposes a long-term stock price series forecasting method based on PSO-Informer. First, 43 kinds of technical indicator factors and K-line data were selected to construct the input data, and then the PSO-Informer model was used to predict the future 60 time points of the stock closing price. In the model training process, the particle swarm algorithm is used to optimize the parameters of the Informer network. Based on the five-minute K-line data of the SSE 50 stock index and the CSI 300 stock index, experimental research was conducted respectively. Taking the accuracy of the long-term stock price prediction overall trend as the evaluation index, and the prediction accuracy reaches 68.2% and 67.5% respectively. The comparison experiments with ARIMA, Prophet, PSO-LSTM and Informer prediction models show that the model has the best performance and is practical.

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Section: Informer Model For Long-term Stock Price Predictionmentioning

confidence: 99%

Long-term stock price forecast based on PSO-informer model

Liu

Deng²,

Wei³

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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“…Therefore, when predicting the hydraulic support liquid demand, a sequence of actions before the hydraulic support needs to be considered. LSTM is a commonly used time series prediction model, and various improved variants have been widely used in predicting power loads, equipment life, and commodity prices [6,7] . Studies have shown that the Transformer Model has parallel computing advantages and exhibits better long-term memory capabilities than LSTM in long-time series model prediction.…”

Section: Related Workmentioning

confidence: 99%

Long-distance intelligent liquid supply for coal mining faces based on liquid demand prediction

Wen-shu

Lai

et al. 2023

J. Phys.: Conf. Ser.

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The coal mine pump station liquid supply system adapts to changes in the main pressure for supply, but under conditions of long-distance liquid supply beyond 2000 m, there is a problem with the difficult system response to the hydraulic support liquid demand, resulting in significant pressure fluctuations in the hydraulic system and serious performance degradation of the supply system. To solve this problem, a long-distance intelligent liquid supply method based on liquid demand prediction for coal mining faces is proposed to ensure stable liquid supply. The method integrates parameters such as the mining machine’s position, the hydraulic support’s column pressure before and after the mining machine, the forward travel distance, and action codes to construct a feature vector. Based on the Transformer Model, a Liquid Demand Prediction Model for hydraulic support is established. Attention mechanisms are combined to solve the problem of time prediction for long sequences. The model is validated using real measured data from a hydraulic system in a coal mine, and the experimental results show that the model can accurately predict liquid demand and achieve long-distance intelligent liquid supply for coal mining faced by combining pump station control strategies.

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“…The banking sphere, headlined by colossal entities like the Industrial and Commercial Bank of China (ICBC) and the Agricultural Bank of China (ABC), commands substantial influence over China's economic fabric. In the digital era, their roles and impact have evolved, with stock performance mirroring not only the health of individual institutions but also the broader economic vitality [5]. Observing the stock trends of these institutions can significantly inform both domestic economic strategies and global financial outlooks.…”

Section: Introductionmentioning

confidence: 99%

Sentiment-Driven Forecasting LSTM Neural Networks for Stock Prediction-Case of China Bank Sector

Jin

2023

IJACSA

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This study explores the predictive analysis of public sentiment in China's financial market, focusing on the banking sector, through the application of machine learning techniques. Specifically, it utilizes the Baidu Index and Long Short-Term Memory (LSTM) networks. The Baidu Index, akin to China's version of Google Trends, serves as a sentiment barometer, while LSTM networks excel in analyzing sequential data, making them apt for stock price forecasting. Our model integrates sentiment indices from Baidu with historical stock data of significant Chinese banks, aiming to unveil how digital sentiment influences stock price movements. The model's forecasting prowess is rigorously evaluated using metrics such as R-squared (R 2 ), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and confusion matrices, the latter being instrumental in assessing the model's capability in correctly predicting stock up or down movements. Our findings predominantly showcase superior prediction performance of the sentiment-based LSTM model compared to a standard LSTM model. However, effectiveness varies across different banks, indicating that sentiment integration enhances prediction capabilities, yet individual stock characteristics significantly contribute to the prediction accuracy. This inquiry not only underscores the importance of integrating public sentiment in financial forecasting models but also provides a pioneering framework for leveraging digital sentiment in financial markets. Through this endeavor, we offer a robust analytical tool for investors, policymakers, and financial institutions, aiding in better navigation through the intricate financial market dynamics, thereby potentially leading to more informed decision-making in the digital age.

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A Data Organization Method for LSTM and Transformer When Predicting Chinese Banking Stock Prices

Cited by 9 publications

References 20 publications

Long-term stock price forecast based on PSO-informer model

Long-term stock price forecast based on PSO-informer model

Long-distance intelligent liquid supply for coal mining faces based on liquid demand prediction

Sentiment-Driven Forecasting LSTM Neural Networks for Stock Prediction-Case of China Bank Sector

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