Time series forecasting is fundamental for various use cases in different domains such as
energy systems and economics. Creating a forecasting model for a specific use case …

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 …

The population growth and climate change are making the agricultural sector to seek more
accurate and efficient approaches to ensure an adequate and regular supply of food for …

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 …

Electroencephalography (EEG) is an exam widely adopted to monitor cerebral activities
regarding external stimuli, and its signals compose a nonlinear dynamical system. There are …

Fuzzy set based time series (FTS) prediction techniques offer potential advantages in
efficient and intuitive data partitioning and the effective handling of uncertainty in the data …

This study elaborates on a novel Hidden Markov Model (HMM)-based fuzzy model for time
series prediction. Fuzzy rules (rule-based models) are employed to describe and quantify …

In the internet of things (IoT), high-dimensional time series data are generated continuously
and recorded from different data sources; moreover, these time series are characterized by …

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 …

Credit scoring is an effective tool for banks and lending companies to manage the potential
credit risk of borrowers. Machine learning algorithms have made grand progress in …