With the advancement of telecommunications, sensor networks, crowd sourcing, and remote
sensing technology in present days, there has been a tremendous growth in the volume of …

In recent years, significant achievements have been made in time series forecasting using
deep learning methods, particularly the Long Short-Term Memory Network (LSTM) …

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 …

Correlated multivariate time series prediction is an effective tool for discovering the chang
rules of temporal data, but it is challenging to find these rules. Recently, deep learning …

Prediction of spatial raster time series, especially those obtained from satellite remote
sensing imagery, plays a crucial role in monitoring various complex spatio-temporal …

Recent development of Industrial Internet produces a large number of sensor data that
record the production status of equipment in the field of industrial intelligence. This paper …

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 …

Motivated by the inherent potentials of the probabilistic modeling with Bayesian network
(BN), this monograph highlights on some crucial as well as practical issues in spatial time …

While the previous chapters keep focus on the working principles and performances of
variants of enhanced BN models, this chapter presents the BN models from the perspective …