Stock price prediction using deep learning and frequency decomposition

DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index

(2020) Imam Mustafa Kamal et al.   Applied Sciences-Basel

A novel Deep Learning Framework: Prediction and Analysis of Financial Time Series using CEEMD and LSTM

(2020) Yong'an Zhang et al.   EXPERT SYSTEMS WITH APPLICATIONS

Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts

(2020) Mumtaz Ali et al.   JOURNAL OF HYDROLOGY

A CNN–LSTM model for gold price time-series forecasting

(2020) Ioannis E. Livieris et al.   NEURAL COMPUTING & APPLICATIONS

A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization

(2020) R. Ahmed et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Reducing Exchange Rate Risks in International Trade: A Hybrid Forecasting Approach of CEEMDAN and Multilayer LSTM

(2020) Hualing Lin et al.   Sustainability

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

Deep Learning for Stock Market Prediction

(2020) M. Nabipour et al.   Entropy

CNNpred: CNN-based stock market prediction using a diverse set of variables

(2019) Ehsan Hoseinzade et al.   EXPERT SYSTEMS WITH APPLICATIONS

Stock price forecasting model based on modified convolution neural network and financial time series analysis

(2019) Jiasheng Cao et al.   INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS

Dynamic relationship analysis between NAFTA stock markets using nonlinear, nonparametric, non-stationary methods

(2019) Josué M. Polanco-Martínez   NONLINEAR DYNAMICS

Advances and opportunities in machine learning for process data analytics

(2019) S. Joe Qin et al.   COMPUTERS & CHEMICAL ENGINEERING

Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting

(2019) Le et al.   Water

Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting

(2019) Wei et al.   Energies

Stock closing price prediction based on sentiment analysis and LSTM

(2019) Zhigang Jin et al.   NEURAL COMPUTING & APPLICATIONS

A novel framework for daily forecasting of ozone mass concentrations based on cycle reservoir with regular jumps neural networks

(2019) Yuqin Mo et al.   ATMOSPHERIC ENVIRONMENT

Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach

(2018) Omer Berat Sezer et al.   APPLIED SOFT COMPUTING

Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries

(2018) Yongzhi Zhang et al.   IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

An innovative neural network approach for stock market prediction

(2018) Xiongwen Pang et al.   JOURNAL OF SUPERCOMPUTING

Deep belief network based k-means cluster approach for short-term wind power forecasting

(2018) Kejun Wang et al.   ENERGY

Improved EEMD-based crude oil price forecasting using LSTM networks

(2018) Yu-Xi Wu et al.   PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS

Financial time series forecasting model based on CEEMDAN and LSTM

(2018) Jian Cao et al.   PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS

Accurate photovoltaic power forecasting models using deep LSTM-RNN

(2017) Mohamed Abdel-Nasser et al.   NEURAL COMPUTING & APPLICATIONS

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

(2017) Wei Bao et al.   PLoS One

Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning

(2013) Marco Lippi et al.   IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS