Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022
出版年份 2023 全文链接
标题
Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022
作者
关键词
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出版物
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
Volume -, Issue -, Pages -
出版商
Wiley
发表日期
2023-09-28
DOI
10.1002/widm.1519
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Forecasting cryptocurrency prices using Recurrent Neural Network and Long Short-term Memory
- (2022) I. Nasirtafreshi DATA & KNOWLEDGE ENGINEERING
- QuantumLeap: Hybrid quantum neural network for financial predictions
- (2022) Eric Paquet et al. EXPERT SYSTEMS WITH APPLICATIONS
- A convolutional neural network based approach to financial time series prediction
- (2022) Dr. M. Durairaj et al. NEURAL COMPUTING & APPLICATIONS
- A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model
- (2022) Xinchen Zhang et al. Computational Intelligence and Neuroscience
- Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition
- (2022) Changrui Deng et al. INFORMATION SCIENCES
- Review of automated time series forecasting pipelines
- (2022) Stefan Meisenbacher et al. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
- Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period
- (2022) Abdullah Bin Omar et al. Frontiers in Environmental Science
- Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm
- (2022) Chunying Wu et al. RESOURCES POLICY
- CNN-GRUA-FC Stock Price Forecast Model Based on Multi-Factor Analysis
- (2022) Shuying Yang et al. Journal of Advanced Computational Intelligence and Intelligent Informatics
- Forecasting oil commodity spot price in a data-rich environment
- (2022) Sabri Boubaker et al. ANNALS OF OPERATIONS RESEARCH
- A new decomposition ensemble model for stock price forecasting based on system clustering and particle swarm optimization
- (2022) Yuqi Guo et al. APPLIED SOFT COMPUTING
- Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks
- (2022) Bhaskar Tripathi et al. Computational Economics
- BiCuDNNLSTM-1dCNN — A hybrid deep learning-based predictive model for stock price prediction
- (2022) Anika Kanwal et al. EXPERT SYSTEMS WITH APPLICATIONS
- A survey on machine learning models for financial time series forecasting
- (2022) Yajiao Tang et al. NEUROCOMPUTING
- An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting
- (2022) Gourav Kumar et al. SOFT COMPUTING
- Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms
- (2022) Theyazn H. H. Aldhyani et al. Electronics
- A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction
- (2022) Yu Lin et al. RESOURCES POLICY
- Forecasting cryptocurrencies’ price with the financial stress index: a graph neural network prediction strategy
- (2022) Wei Yin et al. APPLIED ECONOMICS LETTERS
- Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit
- (2022) Chuen Yik Kang et al. Data
- An Advanced CNN-LSTM Model for Cryptocurrency Forecasting
- (2021) Ioannis E. Livieris et al. Electronics
- LSTM-based sentiment analysis for stock price forecast
- (2021) Ching-Ru Ko et al. PeerJ Computer Science
- Integrating big data driven sentiments polarity and ABC-optimized LSTM for time series forecasting
- (2021) Raghavendra Kumar et al. MULTIMEDIA TOOLS AND APPLICATIONS
- A Stock Closing Price Prediction Model Based on CNN-BiSLSTM
- (2021) Haiyao Wang et al. COMPLEXITY
- Prediction of Financial Time Series Based on LSTM Using Wavelet Transform and Singular Spectrum Analysis
- (2021) Qi Tang et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- Stock Price Forecast Based on CNN-BiLSTM-ECA Model
- (2021) Yu Chen et al. Scientific Programming
- Research on a hybrid prediction model for stock price based on long short-term memory and variational mode decomposition
- (2021) Yang Yujun et al. SOFT COMPUTING
- A New Hybrid Forecasting Model Based on SW-LSTM and Wavelet Packet Decomposition: A Case Study of Oil Futures Prices
- (2021) Jie Wang et al. Computational Intelligence and Neuroscience
- Stock-Price Forecasting Based on XGBoost and LSTM
- (2021) Pham Hoang Vuong et al. COMPUTER SYSTEMS SCIENCE AND ENGINEERING
- Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling
- (2021) Wei Li et al. ENERGY
- Applications of deep learning in stock market prediction: Recent progress
- (2021) Weiwei Jiang EXPERT SYSTEMS WITH APPLICATIONS
- Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels
- (2021) Zhuorui Zhang et al. EXPERT SYSTEMS WITH APPLICATIONS
- Better Effectiveness of Multi-Integrated Neural Networks: Take Stock Big Data as an Example
- (2021) HangLin Lu et al. WIRELESS COMMUNICATIONS & MOBILE COMPUTING
- Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions
- (2021) Nusrat Rouf et al. Electronics
- An Optimal Weighted Combined Model Coupled with Feature Reconstruction and Deep Learning for Multivariate Stock Index Forecasting
- (2021) Jujie Wang et al. Mathematics
- A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
- (2021) Zewen Li et al. IEEE Transactions on Neural Networks and Learning Systems
- Forecasting stock prices with long-short term memory neural network based on attention mechanism
- (2020) Jiayu Qiu et al. PLoS One
- Training deep quantum neural networks
- (2020) Kerstin Beer et al. Nature Communications
- Financial time series forecasting with deep learning : A systematic literature review: 2005–2019
- (2020) Omer Berat Sezer et al. APPLIED SOFT COMPUTING
- Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks
- (2020) Hualing Lin et al. Energies
- A CNN–LSTM model for gold price time-series forecasting
- (2020) Ioannis E. Livieris et al. NEURAL COMPUTING & APPLICATIONS
- A new financial data forecasting model using genetic algorithm and long short-term memory network
- (2020) Yusheng Huang et al. NEUROCOMPUTING
- Enhancing profit from stock transactions using neural networks
- (2020) Ahana Roy Choudhury et al. AI COMMUNICATIONS
- A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network
- (2020) Hongli Niu et al. APPLIED INTELLIGENCE
- Using Deep Learning for price prediction by exploiting stationary limit order book features
- (2020) Avraam Tsantekidis et al. APPLIED SOFT COMPUTING
- An improved deep learning model for predicting stock market price time series
- (2020) Hui Liu et al. DIGITAL SIGNAL PROCESSING
- Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting
- (2020) Tong Niu et al. EXPERT SYSTEMS WITH APPLICATIONS
- Prediction model of energy market by long short term memory with random system and complexity evaluation
- (2020) Yu Yang et al. APPLIED SOFT COMPUTING
- Forecasting Stock Market Index Based on Pattern-Driven Long Short-Term Memory
- (2020) SONG DONGHWAN et al. Economic Computation and Economic Cybernetics Studies and Research
- Stock market movement forecast: A Systematic review
- (2020) O Bustos et al. EXPERT SYSTEMS WITH APPLICATIONS
- Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods
- (2020) Saeed Nosratabadi et al. Mathematics
- A CNN-LSTM-Based Model to Forecast Stock Prices
- (2020) Wenjie Lu et al. COMPLEXITY
- Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model
- (2020) Jingmiao Li et al. ENERGY
- Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions
- (2020) Ankit Thakkar et al. Information Fusion
- An Experimental Review on Deep Learning Architectures for Time Series Forecasting
- (2020) Pedro Lara-Benitez et al. International Journal of Neural Systems
- A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions
- (2020) Mohil Maheshkumar Patel et al. Journal of Information Security and Applications
- Energy futures price prediction and evaluation model with deep bidirectional gated recurrent unit neural network and RIF-based algorithm
- (2020) Bin Wang et al. ENERGY
- Stock price prediction using deep learning and frequency decomposition
- (2020) Hadi Rezaei et al. EXPERT SYSTEMS WITH APPLICATIONS
- DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
- (2019) Zihao Zhang et al. IEEE TRANSACTIONS ON SIGNAL PROCESSING
- Stock price prediction based on deep neural networks
- (2019) Pengfei Yu et al. NEURAL COMPUTING & APPLICATIONS
- A systematic review of fundamental and technical analysis of stock market predictions
- (2019) Isaac Kofi Nti et al. ARTIFICIAL INTELLIGENCE REVIEW
- Stock closing price prediction based on sentiment analysis and LSTM
- (2019) Zhigang Jin et al. NEURAL COMPUTING & APPLICATIONS
- Methods for interpreting and understanding deep neural networks
- (2018) Grégoire Montavon et al. DIGITAL SIGNAL PROCESSING
- An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition
- (2017) Baoguang Shi et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Energy forecasting tools and services
- (2017) Jorge Á. González Ordiano et al. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
- Computational Intelligence and Financial Markets: A Survey and Future Directions
- (2016) Rodolfo C. Cavalcante et al. EXPERT SYSTEMS WITH APPLICATIONS
- Description and prediction of time series: A general framework of Granular Computing
- (2015) Rami Al-Hmouz et al. EXPERT SYSTEMS WITH APPLICATIONS
- Time series forecasting for nonlinear and non-stationary processes: a review and comparative study
- (2015) Changqing Cheng et al. IIE TRANSACTIONS
- A combination of artificial neural network and random walk models for financial time series forecasting
- (2013) Ratnadip Adhikari et al. NEURAL COMPUTING & APPLICATIONS
- The Graph Neural Network Model
- (2008) F. Scarselli et al. IEEE TRANSACTIONS ON NEURAL NETWORKS
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