A CNN-Bi_LSTM parallel network approach for train travel time prediction
Convolutional neural networks (CNNs) offer a broad technical framework to deal with spatial
feature extraction and nonlinearity capture, whereas they cannot process sequence data …
feature extraction and nonlinearity capture, whereas they cannot process sequence data …
[HTML][HTML] The connections between COVID-19 and the energy commodities prices: evidence through the Dynamic Time Warping method
The main objective of the study is to assess the similarity between the time series of energy
commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic …
commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic …
[HTML][HTML] Incorporating causality in energy consumption forecasting using deep neural networks
Forecasting energy demand has been a critical process in various decision support systems
regarding consumption planning, distribution strategies, and energy policies. Traditionally …
regarding consumption planning, distribution strategies, and energy policies. Traditionally …
[HTML][HTML] Forecasting cryptocurrencies volatility using statistical and machine learning methods: A comparative study
Forecasting cryptocurrency volatility can help investors make better-informed investment
decisions in order to minimize risks and maximize potential profits. Accurate forecasting of …
decisions in order to minimize risks and maximize potential profits. Accurate forecasting of …
TBDQN: A novel two-branch deep Q-network for crude oil and natural gas futures trading
Z Huang, W Gong, J Duan - Applied Energy, 2023 - Elsevier
Algorithmic trading plays a significant role in the trade of crude oil and natural gas futures. In
this paper we propose a novel deep reinforcement learning (DRL) algorithm, dubbed two …
this paper we propose a novel deep reinforcement learning (DRL) algorithm, dubbed two …
[HTML][HTML] Covariance matrix forecasting using support vector regression
P Fiszeder, W Orzeszko - Applied intelligence, 2021 - Springer
Support vector regression is a promising method for time-series prediction, as it has good
generalisability and an overall stable behaviour. Recent studies have shown that it can …
generalisability and an overall stable behaviour. Recent studies have shown that it can …
[HTML][HTML] Climate risks and the realized volatility oil and gas prices: results of an out-of-sample forecasting experiment
R Gupta, C Pierdzioch - Energies, 2021 - mdpi.com
We extend the widely-studied Heterogeneous Autoregressive Realized Volatility (HAR-RV)
model to examine the out-of-sample forecasting value of climate-risk factors for the realized …
model to examine the out-of-sample forecasting value of climate-risk factors for the realized …
[HTML][HTML] Efficient machine learning model to predict fineness, in a vertical raw meal of Morocco cement plant
F Belmajdoub, S Abderafi - Results in Engineering, 2023 - Elsevier
Soft sensor enables computing parameters that can be physically impossible to measure.
This work aims to develop a soft sensor for raw meal fineness in a vertical roller mill of a …
This work aims to develop a soft sensor for raw meal fineness in a vertical roller mill of a …
[HTML][HTML] Tail dependence between crude oil volatility index and WTI oil price movements during the COVID-19 pandemic
This study investigates the dependence between extreme returns of West Texas
Intermediate (WTI) crude oil prices and the Crude Oil Volatility Index (OVX) changes as well …
Intermediate (WTI) crude oil prices and the Crude Oil Volatility Index (OVX) changes as well …
[HTML][HTML] Nonlinear causality between crude oil prices and exchange rates: Evidence and forecasting
W Orzeszko - Energies, 2021 - mdpi.com
The relationships between crude oil prices and exchange rates have always been of interest
to academics and policy analysts. There are theoretical transmission channels that justify …
to academics and policy analysts. There are theoretical transmission channels that justify …