Fuzzy time series forecasting methods do not require constraints found in conventional approaches. In addition, due to uncertainty that they contain, many time series to be forecasted should be considered as fuzzy time series. Fuzzy time series forecasting models consist of three steps as fuzzification, identification of fuzzy relations and defuzzification. Although most of the time series enco...

This article examines four problems with past evaluations of presidential election forecasting and suggests one aspect of the models that could be improved. Past criticism has had problems with establishing an overall appraisal of the forecasting equations, in assessing the accuracy of both the forecasting models and their forecasts of individual election results, in identifying the theoretical...

We develop forecasting models based on the neural approach for the forecasting of the bond price time series provided by the VUB bank and make their comparisons of the forecast accuracy with the class of the statistical ARCH-GARCH models. There is a limited statistical or computer science theory on how to design the architecture of the RBF networks for some specific nonlinear financial or econo...

A new era of data analytics of online social networks promises tremendous high-impact societal, business, and healthcare applications. As more users join online social networks, the data available for analysis and forecast of human social and collective behavior grows at an incredible pace. The first part of this talk introduces an apparent paradox, where larger online social networks entail mo...

Two evaluative criteria for probabilistic forecasting performance, consistency with the axioms of probability theory and external correspondence with the events that ultimately occur, are distinguished. The mean probability, or Brier score (PS), is the scoring rule most commonly used to quantify external correspondence. A review is made of methods for decomposing PS into components that represe...

Social scientists envy the objectivity, controlled experimentation and replicability of hard sciences, a lack of which, they daim, hampers their ability to advance their disciplines and make them more useful and relevant to real life applications. This paper examines a specific area of social science, time series forecasting, which, through empirical studies using real-life data, allows for obj...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

abstract forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. this paper studies load consumption modeling in hamedan city province distribution network by applying esn neural network. weather forecasting data such as minimum day temperature, average day temp...