Forecasting stock prices with long-short term memory neural network based on attention mechanism.
The stock market is known for its extreme complexity and volatility, and people are always looking for an accurate and effective way to guide stock trading. Long short-term memory (LSTM) neural networks are developed by recurrent neural networks (RNN) and have significant application value in many f...
Saved in:
| Main Authors: | Jiayu Qiu, Bin Wang, Changjun Zhou |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0227222&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Forecasting stock prices using long short-term memory involving attention approach: An application of stock exchange industry.
by: Muhammad Idrees, et al.
Published: (2025-01-01) -
Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory
by: Xiaolu Wei, et al.
Published: (2020-01-01) -
THE COMPARISON OF LONG SHORT-TERM MEMORY AND BIDIRECTIONAL LONG SHORT-TERM MEMORY FOR FORECASTING COAL PRICE
by: Indra Rivaldi Siregar, et al.
Published: (2025-01-01) -
A dual-path convolutional neural network combined with an attention-based bidirectional long short-term memory network for stock price prediction.
by: Guiyan Zhao, et al.
Published: (2025-01-01) -
Forecasting Stock Returns Using Long Short-Term Memory (LSTM) Model Based on Inflation Data and Historical Stock Price Movements
by: Nur Faid Prasetyo, et al.
Published: (2025-05-01)