For many decades, time-series forecasting has been applied to different problems by scientists and
industries. Many models have been introduced for the purpose of forecasting. Statistical techniques
have been applied to this task since many years ago, after which neural network algorithms were
introduced. Today, hybrid techniques are gaining popularity, aiming to put together the advantages of
these two approaches. These hybrid approaches can provide better forecasting, and at the same
time, they can develop a more sophisticated set of visualization analytics for decision or decision
support. And recently, the application of entropy and fuzzy logic in hybrid forecasting makes the
modeling performed by these advanced systems more capable of modeling complex and uncertain
situations in financial markets as well as in the energy market domain. These techniques turn out to
be effective in increasing the accuracy of forecasting and in decision-making processes, with growing
importance in various applications, as noticed in our paper.