Energy price prediction using data-driven models: A decade review
The accurate prediction of energy price is critical to the energy market orientation, and it can
provide a reference for policymakers and market participants. In practice, energy prices are …
provide a reference for policymakers and market participants. In practice, energy prices are …
Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization
Estimating the price of crude oil, which is seen as an important resource for economic
development and stability in the world, is a topic of great interest by policy makers and …
development and stability in the world, is a topic of great interest by policy makers and …
A systematic literature review on price forecasting models in construction industry
This paper summarizes a list of previously used forecasting models in the construction
industry, using a three-stage review process. Specifically, articles published between 2012 …
industry, using a three-stage review process. Specifically, articles published between 2012 …
Bitcoin price forecasting with neuro-fuzzy techniques
GS Atsalakis, IG Atsalaki, F Pasiouras… - European journal of …, 2019 - Elsevier
Cryptocurrencies, with Bitcoin being the most notable example, have attracted considerable
attention in recent years, and they have experienced large fluctuations in their price. While a …
attention in recent years, and they have experienced large fluctuations in their price. While a …
A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction
Y Hu, J Ni, L Wen - Physica A: Statistical Mechanics and its Applications, 2020 - Elsevier
Forecasting the copper price volatility is an important yet challenging task. Given the
nonlinear and time-varying characteristics of numerous factors affecting the copper price, we …
nonlinear and time-varying characteristics of numerous factors affecting the copper price, we …
A novel hybrid method for crude oil price forecasting
JL Zhang, YJ Zhang, L Zhang - Energy Economics, 2015 - Elsevier
Forecasting crude oil price is a challenging task. Given the nonlinear and time-varying
characteristics of international crude oil prices, we propose a novel hybrid method to …
characteristics of international crude oil prices, we propose a novel hybrid method to …
Gold price volatility: A forecasting approach using the Artificial Neural Network–GARCH model
W Kristjanpoller, MC Minutolo - Expert systems with applications, 2015 - Elsevier
One of the most used methods to forecast price volatility is the generalized autoregressive
conditional heteroskedasticity (GARCH) model. Nonetheless, the errors in prediction using …
conditional heteroskedasticity (GARCH) model. Nonetheless, the errors in prediction using …
Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization …
T Zhang, Z Tang, J Wu, X Du, K Chen - Energy, 2021 - Elsevier
The prediction of crude oil prices has important research significance. The paper contributes
to the literature of hybrid models for forecasting crude oil prices. We apply ensemble …
to the literature of hybrid models for forecasting crude oil prices. We apply ensemble …
Forecasting volatility of oil price using an artificial neural network-GARCH model
W Kristjanpoller, MC Minutolo - Expert Systems with Applications, 2016 - Elsevier
This paper builds on previous research and seeks to determine whether improvements can
be achieved in the forecasting of oil price volatility by using a hybrid model and …
be achieved in the forecasting of oil price volatility by using a hybrid model and …
Oil price forecasting using a hybrid model
A Safari, M Davallou - Energy, 2018 - Elsevier
Forecasting oil prices is an important and challenging matter, because of its impact on many
economic and non-economic factors. Because factors such as economic growth, political …
economic and non-economic factors. Because factors such as economic growth, political …