Recent advances in technology have brought major breakthroughs in data collection,
enabling a large amount of data to be gathered over time and thus generating time series …

Deep generative models have demonstrated their effectiveness in learning latent
representation and modeling complex dependencies of time series. In this article, we …

The deep learning model has been used to predict the variation rule of the target series of
multivariate time series data. Based on the attention mechanism, the influence information of …

Urban sensors are an important part of urban infrastructures and are usually heterogeneous.
Urban sensors with different uses vary greatly in hardware structure, communication …

In the multivariate time series prediction tasks, the impact information of all nonpredictive
time series on the predictive target series is difficult to be extracted at different time stages …

With the digitalization of electricity meters many previously solved security problems, such
as electricity theft, are reintroduced as IT related challenges which require modern detection …

In modern data analysis, time is often considered just another feature. Yet time has a special
role that is regularly overlooked. Procedures are usually only designed for time-independent …

The fields of machine learning and deep learning witnessed significant advances in the past
few decades. However, progress in the development of methods to analyze sequential data …

In this article, we propose Palisade, a distributed framework for streaming anomaly
detection. Palisade is motivated by the need to apply multiple detection algorithms for …

Local differential privacy (LDP), which enables an untrusted server to collect aggregated
statistics from distributed users while protecting the privacy of those users, has been widely …