Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From China

Tengxi, Wang

doi:10.1109/access.2023.3309007

IEEE Access

2023

DOI: 10.1109/access.2023.3309007

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Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From China

Wang Tengxi

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

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An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm

Sun,

Hong,

et al. 2024

Sensors

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Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for the noninvasive diagnosis of arrhythmia. In this paper, we propose an arrhythmia classification model based on the combination of a channel attention mechanism (SE module), convolutional neural network (CNN), and long short-term memory neural network (LSTM). The data of this model use the MIT-BIH arrhythmia database, and after noise reduction of raw ECG data by the EEMD denoising algorithm, a CNN-LSTM is used to learn features from the data, and the fusion channel attention mechanism is used to adjust the weight of the feature map. The CNN-LSTM-SE model is compared with the LSTM, CNN-LSTM, and LSTM-attention models, and the models are evaluated using Precision, Recall, and F1-Score. The classification performance of the tested CNN-LSTM-SE classification prediction model is better, with a classification accuracy of 98.5%, a classification precision rate of more than 97% for each label, a recall rate of more than 98%, and an F1-score of more than 0.98. It meets the requirements of arrhythmia classification prediction and has a certain practical value.

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An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm

Sun,

Hong,

et al. 2024

Sensors

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show abstract

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Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From China

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 1 publication

References 63 publications

An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm

An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm

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