Recently, there seems to be increased interest in time series forecasting using soft
computing (SC) techniques, such as fuzzy sets, artificial neural networks (ANNs), rough set …

Time Series is an orderly sequence of values of a variable in a particular domain.
Forecasting is a challenging task in the area of Time Series Analysis. Forecasting has a …

In this article, a novel M-factors fuzzy time series (FTS) forecasting model is presented,
which relies upon on the hybridization of two procedures, viz., granular computing and bio …

The study shows that there are two main problems that affect the performance of fuzzy time
series (FTS) models, namely the selection of the universe of discourse and the …

We explore the short-run forecasting problem at horizons of 1, 5, 10, 15, and 20 days for
three forecasting periods within one year for the Chinese stock index from April 16, 2010, the …

Time series forecasting is a powerful tool in planning and decision making, from traditional
statistical models to soft computing and artificial intelligence approaches several methods …

Recently, many fuzzy time series models have already been used to solve nonlinear and
complexity issues. However, first-order fuzzy time series models have proven to be …

This book focuses on the fundamentals of deep learning along with reporting on the current
state-of-art research on deep learning. In addition, it provides an insight of deep neural …

In real time, one observation always relies on several observations. To improve the
forecasting accuracy, all these observations can be incorporated in forecasting models …

The time series prediction models based on fuzzy set theory have been widely applied to
diverse fields such as enrollments, stocks, weather and etc., as they can handle prediction …