A Stock Price Foresting Using LSTM Based on Attention Mechanism
- DOI
- 10.2991/978-94-6463-052-7_162How to use a DOI?
- Keywords
- Stock price prediction; LSTM; Attention mechanism; Rate change
- Abstract
Stock price prediction has been a hit subject in recent decades. Many researchers find different methods to predict stock price. LSTM is an excellent variant model of RNN, but single LSTM can only process a single form of data and lacks the ability to process multiple mixed forms of data. Considering that stocks represent the financial market, the exchange rate would have a particular impact on the financial market, so rate change affects stock price movement. Therefore, attention mechanism could introduce exchange rate into LSTM, so we produce a hybrid LSTM module based on attention mechanism to predict stock price. We find that the RMSE and MSE of hybrid LSTM are lower than others.
- Copyright
- © 2022 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xiaofei Wu PY - 2022 DA - 2022/12/27 TI - A Stock Price Foresting Using LSTM Based on Attention Mechanism BT - Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022) PB - Atlantis Press SP - 1467 EP - 1476 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-052-7_162 DO - 10.2991/978-94-6463-052-7_162 ID - Wu2022 ER -