Deep learning framework for predictive modeling of crude oil price for sustainable management in oil markets

A hybrid RF-LSTM based on CEEMDAN for improving the accuracy of building energy consumption prediction

(2022) Irene Karijadi et al.   ENERGY AND BUILDINGS

Carbon price forecasting based on CEEMDAN and LSTM

(2022) Feite Zhou et al.   APPLIED ENERGY

Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks

(2021) K.U. Jaseena et al.   ENERGY CONVERSION AND MANAGEMENT

Online Learning: A Comprehensive Survey

(2021) Steven C. H. Hoi et al.   NEUROCOMPUTING

Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

(2021) Jesus Lago et al.   APPLIED ENERGY

A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting

(2021) Siddhaling Urolagin et al.   ENERGY

Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm

(2021) Tingting Zhang et al.   ENERGY

Constructing a stock-price forecast CNN model with gold and crude oil indicators

(2021) Yu-Chen Chen et al.   APPLIED SOFT COMPUTING

Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance

(2021) Ganiyu Adewale Busari et al.   COMPUTERS & CHEMICAL ENGINEERING

Linkages between the international crude oil market and the Chinese stock market: A BEKK-GARCH-AFD approach

(2021) Qiwei Xie et al.   ENERGY ECONOMICS

Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization

(2021) Seçkin Karasu et al.   ENERGY

Multi-Horizon Dependence between Crude Oil and East Asian Stock Markets and Implications in Risk Management

(2020) Xiaojing Cai et al.   Energies

SciPy 1.0: fundamental algorithms for scientific computing in Python

(2020) Pauli Virtanen et al.   NATURE METHODS

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

(2020) Pedro Lara-Benítez et al.   Applied Sciences-Basel

A novel hybrid model for forecasting crude oil price based on time series decomposition

(2020) Hooman Abdollahi   APPLIED ENERGY

Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM

(2020) Melike Bildirici et al.   Energies

A new hybrid model for forecasting Brent crude oil price

(2020) Hooman Abdollahi et al.   ENERGY

Ensemble probabilistic prediction approach for modeling uncertainty in crude oil price

(2020) Jianzhou Wang et al.   APPLIED SOFT COMPUTING

Copper Price Prediction Using Support Vector Regression Technique

(2020) Gabriel Astudillo et al.   Applied Sciences-Basel

Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model

(2020) Jingmiao Li et al.   ENERGY

Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems

(2020) Linfei Yin et al.   APPLIED ENERGY

Federated learning for machinery fault diagnosis with dynamic validation and self-supervision

(2020) Wei Zhang et al.   KNOWLEDGE-BASED SYSTEMS

Stock price prediction using deep learning and frequency decomposition

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

A Learning System Integrating Temporal Convolution and Deep Learning for Predictive Modeling of Crude Oil Price

(2020) Tong Niu et al.   IEEE Transactions on Industrial Informatics

Deep Reinforcement Learning for Multiobjective Optimization

(2020) Kaiwen Li et al.   IEEE Transactions on Cybernetics

The effects of oil price volatility on ethanol, gasoline, and sugar price forecasts

(2019) Lucio Guido Tapia Carpio   ENERGY

A decomposition–integration model with dynamic fuzzy reconstruction for crude oil price prediction and the implications for sustainable development

(2019) Jian Chai et al.   JOURNAL OF CLEANER PRODUCTION

Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network

(2019) Yishun Liu et al.   KNOWLEDGE-BASED SYSTEMS

Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature

(2019) Zhiqiang Geng et al.   ENERGY

Probabilistic Forecasting With Fuzzy Time Series

(2019) Petronio Candido de Lima Silva et al.   IEEE TRANSACTIONS ON FUZZY SYSTEMS

A semi-heterogeneous approach to combining crude oil price forecasts

(2018) Jue Wang et al.   INFORMATION SCIENCES

Beta autoregressive fractionally integrated moving average models

(2018) Guilherme Pumi et al.   JOURNAL OF STATISTICAL PLANNING AND INFERENCE

A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation

(2018) Griet Goovaerts et al.   IEEE Journal of Biomedical and Health Informatics

Variational Mode Decomposition

(2014) Konstantin Dragomiretskiy et al.   IEEE TRANSACTIONS ON SIGNAL PROCESSING

Conditional Variable Importance for Random Forests

(2008) Carolin Strobl et al.   BMC BIOINFORMATICS

Fractional dynamic behavior in Forcados Oil Price Series: An application of detrended fluctuation analysis

(2008) O. Felix Ayadi et al.   Energy for Sustainable Development