Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …

Among various soft computing approaches for time series forecasting, fuzzy cognitive maps
(FCMs) have shown remarkable results as a tool to model and analyze the dynamics of …

In recent years, the demand for developing low computational cost methods to deal with
uncertainties in forecasting has been increased. Probabilistic forecasting is a class of …

Fuzzy time series (FTS) forecasting models show a great performance in predicting time
series, such as air pollution time series. However, they have caused major issues by utilizing …

Time series arise in many fields of science such as engineering, economy and agriculture to
cite a few. In the early 1990's the so called Fuzzy Time Series were proposed to handle …

Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …

Time series forecasting is an essential task in the management of Smart Cities and Smart
Grids, becoming even more challenging when it needs to deal with big data time series. The …

This article presents a higher-order circular intuitionistic fuzzy time series forecasting method
for predicting the stock change index, which is shown to be an improvement over traditional …

No campo da previsão de séries temporais os métodos mais difundidos baseiam-se em
predição por ponto. Esse tipo de previsão, no entanto, tem um sério inconveniente: ele não …

This paper proposes the application of advanced fuzzy time series in order to provide short-
term load forecasting, which consists of predicting future demands from up to a week. An …