As a paradigm of data processing, granular computation concerns processing complex data
entities called granules, which arise from data abstraction and derivation of knowledge from …

Over the years, financial engineering ranging from the study of financial signals to the
modeling of financial prediction is one of the most exciting topics for both academia and …

Evolving participatory learning (ePL) modeling joins the concepts of participatory learning
and evolving fuzzy systems. It uses data streams to continuously adapt the structure and …

Volatility modeling and forecasting play a key role in asset allocation, risk management,
derivatives pricing and policy making. The purpose of this paper is to develop an evolving …

The aim of this research is to develop the procedure of constructing an adaptive neuro-fuzzy
inference system (ANFIS) model for time series data. The procedure of development applies …

Equity assets volatility modeling and forecasting are fundamental in risk management,
portfolio construction, financial decision making and derivative pricing. The use of realized …

Extreme learning machines enable multilayered neural networks to perform activities to
facilitate the process and business dynamics. It acts in pattern classification, linear …

Accurately measuring and forecasting stock market volatility plays a crucial role for asset
and derivative pricing, hedge strategies, portfolio allocation and risk management. Since the …

Recently, prediction modelling has become important in data analysis. In this paper, we
propose a novel algorithm to analyze the past dataset of crop yields and predict future yields …

Financial interval time series (ITS) is a sequence of the highest and lowest values of
financial data such as the highest and lowest prices of assets observed at successive time …