Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022
Accurately predicting the prices of financial time series is essential and challenging for the
financial sector. Owing to recent advancements in deep learning techniques, deep learning …
financial sector. Owing to recent advancements in deep learning techniques, deep learning …
A novel validation framework to enhance deep learning models in time-series forecasting
Time-series analysis and forecasting is generally considered as one of the most challenging
problems in data mining. During the last decade, powerful deep learning methodologies …
problems in data mining. During the last decade, powerful deep learning methodologies …
A convolutional autoencoder topology for classification in high-dimensional noisy image datasets
Deep convolutional neural networks have shown remarkable performance in the image
classification domain. However, Deep Learning models are vulnerable to noise and …
classification domain. However, Deep Learning models are vulnerable to noise and …
A novel forecasting strategy for improving the performance of deep learning models
In this research, a new strategy is introduced for the development of robust, efficient and
reliable deep learning time-series models, which is based on a sophisticated algorithmic …
reliable deep learning time-series models, which is based on a sophisticated algorithmic …
A novel model for spot price forecast of natural gas based on temporal convolutional network
Natural gas is often said to be the most environmentally friendly fossil fuel. Its usage has
increased significantly in recent years. Meanwhile, accurate forecasting of natural gas spot …
increased significantly in recent years. Meanwhile, accurate forecasting of natural gas spot …
A novel multi-step forecasting strategy for enhancing deep learning models' performance
Multi-step forecasting is considered as an open challenge in time-series analysis. Although
several approaches were proposed to address this complex prediction problem, none of …
several approaches were proposed to address this complex prediction problem, none of …
Smoothing and stationarity enforcement framework for deep learning time-series forecasting
Time-series analysis and forecasting problems are generally considered as some of the
most challenging and complicated problems in data mining. In this work, we propose a new …
most challenging and complicated problems in data mining. In this work, we propose a new …
Enhancing trading decision in financial markets: an algorithmic trading framework with continual mean-variance optimization, window presetting, and controlled early …
This study introduces a trading decision support system (DSS) enhanced by an optimized
mean-variance model for algorithmic trading (AT), crucial in modern financial markets for its …
mean-variance model for algorithmic trading (AT), crucial in modern financial markets for its …
TBDQN: A novel two-branch deep Q-network for crude oil and natural gas futures trading
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 …
XSC—an eXplainable image segmentation and classification framework: a case study on skin cancer
Within the field of computer vision, image segmentation and classification serve as crucial
tasks, involving the automatic categorization of images into predefined groups or classes …
tasks, involving the automatic categorization of images into predefined groups or classes …