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 6 publications

(3 citation statements)

References 19 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 long short-term memory network is an RNN proposed by Hochreiter in 1997 [3]. In practical applications, long short-term memory (LSTM) neural networks, which belong to gated RNNs, can learn long-term dependencies more quickly than the simple recurrent architectures [4]. The LSTM architecture handles the vanishing gradient problem.…”

Section: Introductionmentioning

confidence: 99%

Recurrent and Deep Learning Neural Network Models for DDoS Attack Detection

2022

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Distributed denial of service (DDoS) attack is a subclass of denial of service attack that performs severe attack in a cloud computing environment. It makes a malicious attempt to disturb the usual services of any network or server by using botnets. Hence, an efficient intrusion detection system (IDS) is essential to detect this attack. Some limitations in the existing IDS models for DDoS attack detection are delayed convergence, local stagnation issues, and local and global optimal trapping issues. These limitations are met by the recurrent neural network (RNN) and deep learning- (DL-) based proposed models that can utilize the previous states of the hidden neuron. The proposed research has used a long short-term memory (LSTM) recurrent neural network and autoencoder- and decoder-based deep learning strategy with gradient descent learning rule. The network parameters like weight vectors and bias coefficient are tuned optimally by employing the proposed a hybrid Harris Hawks optimization (HHO) and particle swarm optimization (PSO) algorithm. The proposed hybrid optimization algorithm selects the essential attributes, and the results obtained confirmed that the proposed LSTM and deep learning model outperformed all other models developed in the literature.

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

Cited by 6 publications

References 19 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

Recurrent and Deep Learning Neural Network Models for DDoS Attack Detection

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