In recent two decades, artificial neural networks have been extensively used in many
business applications. Despite the growing number of research papers, only few studies …

In the field of business research method, a literature review is more relevant than ever. Even
though there has been lack of integrity and inflexibility in traditional literature reviews with …

Accurate forecasting of volatility from financial time series is paramount in financial decision
making. This paper presents a novel, Particle Swarm Optimization (PSO)-trained Quantile …

The prediction of chaotic time series with neural networks is a traditional practical problem of
dynamic systems. This paper is not intended for proposing a new model or a new …

Financial time series are chaotic that, in turn, leads their predictability to be complex and
challenging. This paper presents a novel financial time series prediction hybrid that involves …

We identify the core topics of research applying machine learning to finance. We use a
probabilistic topic modeling approach to make sense of this diverse body of research …

This study presents a case study of public buildings using a novel deep learning method to
forecast indoor air temperature. The aim is to explore the potential of long short-term …

The traditional Grey Model is easy to understand and simple to calculate, with satisfactory
accuracy, but it is also lack of flexibility to adjust the model to acquire higher forecasting …

This study proposes an ensemble deep learning approach that integrates Bagging Ridge
(BR) regression with Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks …

An accurate exchange rate forecasting and its decision-making to buy or sell are critical
issues in the Forex market. Short-term currency rate forecasting is a challenging task due to …