A new decomposition ensemble model for stock price forecasting based on system clustering and particle swarm optimization

Interval prediction approach to crude oil price based on three-way clustering and decomposition ensemble learning

(2022) Bingzhen Sun et al.   APPLIED SOFT COMPUTING

Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM

(2022) Yanhui Liang et al.   EXPERT SYSTEMS WITH APPLICATIONS

Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting

(2021) Shiwei Su   Journal of Mathematics

A new financial data forecasting model using genetic algorithm and long short-term memory network

(2020) Yusheng Huang et al.   NEUROCOMPUTING

Ensemble learning for electricity consumption forecasting in office buildings

(2020) Tiago Pinto et al.   NEUROCOMPUTING

A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network

(2020) Hongli Niu et al.   APPLIED INTELLIGENCE

A hybrid model for financial time‐series forecasting based on mixed methodologies

(2020) Zhidan Luo et al.   EXPERT SYSTEMS

Analysis of economic growth fluctuations based on EEMD and causal decomposition

(2020) Xuegeng Mao et al.   PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS

A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

(2020) Yang Yujun et al.   COMPLEXITY

The application research of neural network and BP algorithm in stock price pattern classification and prediction

(2020) Dehua Zhang et al.   Future Generation Computer Systems-The International Journal of eScience

Stock price prediction using deep learning and frequency decomposition

(2020) Hadi Rezaei et al.   EXPERT SYSTEMS WITH APPLICATIONS

A machine learning-based forecasting system of perishable cargo flow in maritime transport

(2020) José Antonio Moscoso-López et al.   NEUROCOMPUTING

LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

(2019) Xiaodan Liang et al.   MATHEMATICAL PROBLEMS IN ENGINEERING

Predicting monthly biofuel production using a hybrid ensemble forecasting methodology

(2019) Lean Yu et al.   INTERNATIONAL JOURNAL OF FORECASTING

Multi-objective ensemble forecasting with an application to power transformers

(2018) Abdolrahman Peimankar et al.   APPLIED SOFT COMPUTING

Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models

(2018) Ha Young Kim et al.   EXPERT SYSTEMS WITH APPLICATIONS

An Adaptive SVR for High-Frequency Stock Price Forecasting

(2018) Yanhui Guo et al.   IEEE Access

Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis

(2018) Ranjeeta Bisoi et al.   APPLIED SOFT COMPUTING

An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

(2018) Kexin Bi et al.   CHINESE JOURNAL OF CHEMICAL ENGINEERING

Forecasting price movements using technical indicators: Investigating the impact of varying input window length

(2017) Yauheniya Shynkevich et al.   NEUROCOMPUTING

A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting

(2016) Liang-Ying Wei   APPLIED SOFT COMPUTING

A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting

(2015) Lean Yu et al.   APPLIED ENERGY

Deep learning

(2015) Yann LeCun et al.   NATURE

A review of unsupervised feature learning and deep learning for time-series modeling

(2014) Martin Längkvist et al.   PATTERN RECOGNITION LETTERS

On the computational complexity of the empirical mode decomposition algorithm

(2014) Yung-Hung Wang et al.   PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

(2013) Mohammad Zounemat-kermani et al.   APPLIED SOFT COMPUTING

A hybrid modeling approach for forecasting the volatility of S&P 500 index return

(2011) E. Hajizadeh et al.   EXPERT SYSTEMS WITH APPLICATIONS

Comparison of the finite mixture of ARMA-GARCH, back propagation neural networks and support-vector machines in forecasting financial returns

(2010) Altaf Hossain et al.   JOURNAL OF APPLIED STATISTICS