Stock prediction and analysis using LSTM network

HaoJun Lin; Xiangxian Chen; Suet Yi Chui

doi:10.1117/12.2656805

13 January 2023 Stock prediction and analysis using LSTM network

HaoJun Lin, Xiangxian Chen, Suet Yi Chui

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 125100N (2023) https://doi.org/10.1117/12.2656805
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

Stock prediction is a vital and difficult topic where researchers have explored plentifully. Because of some characteristics of the stock market, like high noise and strong nonlinearity, it is quite hard to forecast the price of stock accurately. Long Short-Term Memory neural network (LSTM) uses input gate, output gate and forgetting gate to control information, and shows excellent performance in processing temporal information.

Citation Download Citation

HaoJun Lin, Xiangxian Chen, and Suet Yi Chui "Stock prediction and analysis using LSTM network", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 125100N (13 January 2023); https://doi.org/10.1117/12.2656805

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