A stock price prediction method based on meta-learning and variational mode decomposition
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A stock price prediction method based on meta-learning and variational mode decomposition
Authors
Keywords
-
Journal
KNOWLEDGE-BASED SYSTEMS
Volume 252, Issue -, Pages 109324
Publisher
Elsevier BV
Online
2022-06-25
DOI
10.1016/j.knosys.2022.109324
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions
- (2020) Xiaoan Yan et al. KNOWLEDGE-BASED SYSTEMS
- A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network
- (2020) Hongli Niu et al. APPLIED INTELLIGENCE
- Multistep-Ahead Stock Price Forecasting Based on Secondary Decomposition Technique and Extreme Learning Machine Optimized by the Differential Evolution Algorithm
- (2020) Zhenpeng Tang et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- DTDR-ALSTM: Extracting dynamic time-delays to reconstruct multivariate data for improving attention-based LSTM industrial time series prediction models
- (2020) Jince Li et al. KNOWLEDGE-BASED SYSTEMS
- CNN-FCM: System modeling promotes stability of deep learning in time series prediction
- (2020) Penghui Liu et al. KNOWLEDGE-BASED SYSTEMS
- A Fourier transform based audio watermarking algorithm
- (2020) Euschi Salah et al. APPLIED ACOUSTICS
- Multistep forecasting for diurnal wind speed based on hybrid deep learning model with improved singular spectrum decomposition
- (2020) Xiaoan Yan et al. ENERGY CONVERSION AND MANAGEMENT
- CNNpred: CNN-based stock market prediction using a diverse set of variables
- (2019) Ehsan Hoseinzade et al. EXPERT SYSTEMS WITH APPLICATIONS
- LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction
- (2019) Xiaodan Liang et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network
- (2019) Yishun Liu et al. KNOWLEDGE-BASED SYSTEMS
- Study on the prediction of stock price based on the associated network model of LSTM
- (2019) Guangyu Ding et al. International Journal of Machine Learning and Cybernetics
- ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module
- (2018) Yujin Baek et al. EXPERT SYSTEMS WITH APPLICATIONS
- Leveraging social media news to predict stock index movement using RNN-boost
- (2018) Weiling Chen et al. DATA & KNOWLEDGE ENGINEERING
- EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction
- (2018) Feng Zhou et al. EXPERT SYSTEMS WITH APPLICATIONS
- A novel double deep ELMs ensemble system for time series forecasting
- (2017) Gang Song et al. KNOWLEDGE-BASED SYSTEMS
- Predicting stock market index using fusion of machine learning techniques
- (2015) Jigar Patel et al. EXPERT SYSTEMS WITH APPLICATIONS
- Variational Mode Decomposition
- (2014) Konstantin Dragomiretskiy et al. IEEE TRANSACTIONS ON SIGNAL PROCESSING
- Empirical Mode Decomposition vs. Wavelet Decomposition for the Extraction of Respiratory Signal From Single-Channel ECG: A Comparison
- (2013) Domenico Labate et al. IEEE SENSORS JOURNAL
- Forecasting tourism demand based on empirical mode decomposition and neural network
- (2011) Chun-Fu Chen et al. KNOWLEDGE-BASED SYSTEMS
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now