In order to approximate the underlying process of temporal data, time series prediction has
been a hot research topic for decades. Develo** predictive models plays an important role …

In the development of smart cities, the intelligent transportation system (ITS) plays a major
role. The dynamic and chaotic nature of the traffic information makes the accurate …

Multivariate Time series data play important roles in our daily life. How to use these data in
the process of prediction is a highly attractive study for many researchers. To achieve this …

Spatial time series prediction is one of the most fascinating areas of modern data science. It
has enormous application in various domains including environmental management …

Bayesian networks (BNs) are one of the key computational models in traditional AI and
machine learning paradigm. These are also considered to belong to the probabilistic …

TCN (Temporal Convolutional Network) has been proved that it outperforms canonical
recurrent networks across different tasks and demonstrates longer effective memory …

With the evolution of smart grids in recent years, load forecasting has received more
research focus than ever before. Several techniques, especially based on artificial neural …

Abstract [eng] This Master's thesis is based on book'Enhanced Bayesian Network Models for
Spatial Time Series Prediction'. Methods written in the mentioned book as Bayesian Network …

One of the important characteristics of Bayesian network is that it can intuitively model the
dependency among numerous variables. However, as the network becomes large …

Abstract Fuzzy Bayesian networks (FBNs) are the variant of standard/classical Bayesian
networks (BNs), which have intrinsic capability of handling ambiguity due to lack of expert …