Search
Search Results
-
Effective Crude Oil Prediction Using CHS-EMD Decomposition and PS-RNN Model
There is an urgent need for the prediction of oil price; in addition, for various small and large industries, individuals, and the government, it is...
-
Increasing the Hong Kong Stock Market Predictability: A Temporal Convolutional Network Approach
Recently, a substantial body of literature in finance has implemented deep learning algorithms as predicting approaches. The principal merit of these...
-
Forecasting bitcoin volatility: exploring the potential of deep learning
This study aims to evaluate forecasting properties of classic methodologies (ARCH and GARCH models) in comparison with deep learning methodologies...
-
Statistical Evaluation of Deep Learning Models for Stock Return Forecasting
Artificial intelligence applications, including algorithmic training, portfolio allocation, and stock return forecasting in the financial industry,...
-
Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data
We examine whether corporate bankruptcy predictions can be improved by utilizing the recurrent neural network (RNN) and long short-term memory (LSTM)...
-
A New Neural Network Approach for Predicting the Volatility of Stock Market
The prediction of stock market volatility is an important and challenging task in the financial market. Recently, neural network approaches have been...
-
Incentive-Based Demand Response with Deep Learning and Reinforcement Learning
Incentive-based Demand Response program that can induce end users to reduce power load during peak load period, has been widely implemented due to...
-
Stock Price Prediction Using Deep Learning
Predicting the stock market trend is gaining increasing attention from both industries as well as academia. However, accurate prediction is difficult...
-
Comparative Study of the Forecasting Solar Energy Generation in Istanbul
The importance of renewable energy sources makes it extremely important day by day due to the limited reserves of fossil fuels and the damage they...
-
High-Performing Machine Learning Algorithms for Predicting the Spread of COVID-19
COVID-19 is a strain of coronavirus that first broke out in Wuhan, China, in December 2019 and has since become a global pandemic. In this chapter,...
-
Navigating the Digital Transformation of Commercial Banks: Embracing Innovation in Customer Emotion Analysis
The rapid development of science has ushered the society into the era of the digital economy, profoundly altering the habits and behaviors of...
-
Exchange Rate Forecasting Based on Integration of Gated Recurrent Unit (GRU) and CBOE Volatility Index (VIX)
The foreign exchange market is the most liquid financial market globally, attracting investors looking for lucrative investment opportunities....
-
PCA-ICA-LSTM: A Hybrid Deep Learning Model Based on Dimension Reduction Methods to Predict S&P 500 Index Price
In this paper, we propose a new hybrid model based on a deep learning network to predict the prices of financial assets. The study addresses two key...
-
Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets
This paper enters the ongoing volatility forecasting debate by examining the ability of a wide range of Machine Learning methods (ML), and...
-
Role of Economic Policy Uncertainty in Energy Commodities Prices Forecasting: Evidence from a Hybrid Deep Learning Approach
Amidst a dynamic energy market landscape, understanding evolving influencing factors is pivotal. Accurate forecasting techniques are indispensable...
-
Brazilian Selic Rate Forecasting with Deep Neural Networks
Artificial intelligence has shortened edges in many areas, especially the economy, to support long-term and accurate forecasting of financial...
-
Deep Learning Model for Fusing Spatial and Temporal Data for Stock Market Prediction
One of the most significant challenges in stock market forecasting is that the majority of stock price analysis and prediction models based on...
-
Comparative analysis of deep-learning-based models for hourly bus passenger flow forecasting
An efficient transportation system is conducive to maintaining traffic flow and safety. Passenger flow forecasting (PFF), an area of traffic...
-
Enhancing Financial Risk Prediction Through Echo State Networks and Differential Evolutionary Algorithms in the Digital Era
In the ever-evolving landscape of financial investment, the digital era has ushered in a new paradigm characterized by technological innovation and...
-
Spatiotemporal localisation patterns of technological startups: the case for recurrent neural networks in predicting urban startup clusters
More attention should be dedicated to intra-urban localisation decisions of technological startups. While the general trend of innovative companies...
