2LE-BO-DeepTrade: an integrated deep learning framework for stock price prediction
This study presents a novel, integrated deep-learning framework named 2LE-BO-DeepTrade for stock closing price prediction. This framework combines 2LE-ICEEMDAN denoising, deep learning models tuned with Bayesian optimization, and a piecewise linear representation (PLR)-based trading strategy. The fr...
Saved in:
| Main Authors: | Zinnet Duygu Akşehir, Erdal Kılıç |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-08-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-3107.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
How to Handle Data Imbalance and Feature Selection Problems in CNN-Based Stock Price Forecasting
by: Zinnet Duygu Aksehir, et al.
Published: (2022-01-01) -
Hybrid Method for Oil Price Prediction Based on Feature Selection and XGBOOST-LSTM
by: Shucheng Lin, et al.
Published: (2025-04-01) -
Intraday and Post-Market Investor Sentiment for Stock Price Prediction: A Deep Learning Framework with Explainability and Quantitative Trading Strategy
by: Guowei Sun, et al.
Published: (2025-05-01) -
DIVIDEND POLICY, TRADING VOLUME AND ORDER IMBALANCE, AND ITS IMPACT ON STOCK PRICE VOLATILITY
by: Putri Elgi Ramadhani, et al.
Published: (2024-12-01) -
Effectiveness of Open, High and Low Prices in Stock Market Price Prediction
by: Collins C. Ngwakwe
Published: (2025-03-01)