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 …

A hybrid model is proposed for the short-term online prediction of tidal currents. The
harmonic residual analysis (HRA) model is designed to augment the numerical schemes …

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 …

This demo paper presents a design and implementation of a system AnomalyKiTS for
detecting anomalies from time series data for the purpose of offering a broad range of …

Air pollution is a worldwide problem faced by most countries across the world. Prediction of
air pollution is crucial in air quality research since it is related to public health effects. The …

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 …

High-dimensional time series increasingly arise in the Internet of Energy (IoE), given the use
of multi-sensor environments and the two way communication between energy consumers …

Time series is an extremely important branch of prediction, and the research on it plays an
important guiding role in production and life. To get more realistic prediction results, scholars …

In the present study, we propose a novel high-order weighted fuzzy time series (FTS)
forecasting method using k-mean clustering, weighted fuzzy logical relations and …

Various studies indicate that Fuzzy Time Series (FTS) methods can obtain high accuracy in
a variety of forecasting applciations. However, weighted FTS methods tend to show …