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- W2890935757 abstract "Long-Short Term Memory Network stands out from the financial sector due to its long-term memory predictability, however, the speed of subsequent operations is extremely slow, and the timeliness of the inability to meet market changes has been criticized. In this paper. Aiming at the shortcomings of the slow running of three gates in each neuron of LSTM in back propagation, we propose to use GA to optimize the internal weights of LSTM neurons to optimize the defect. In this experiment, GA optimization could not change the accuracy of the model, but it achieved better results than the original LSTM in terms of speed, and satisfied the demand of the future business field for rapid response to market changes in terms of timeliness." @default.
- W2890935757 created "2018-09-27" @default.
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- W2890935757 date "2018-06-27" @default.
- W2890935757 modified "2023-10-14" @default.
- W2890935757 title "Short-term prediction of stock market price based on GA optimization LSTM neurons" @default.
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- W2890935757 doi "https://doi.org/10.1145/3234804.3234818" @default.
- W2890935757 hasPublicationYear "2018" @default.
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