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 …

Anomaly detection is a problem with applications for a wide variety of domains; it involves
the identification of novel or unexpected observations or sequences within the data being …

Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …

Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …

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

Due to the continued digitization of industrial and societal processes, including the
deployment of networked sensors, we are witnessing a rapid proliferation of time-ordered …

We analyze multifractality for green bonds, stock sector indices, and US economic sector
bonds. Green bonds and US bonds show non-linear cross-correlations. We perform …

Purpose: This study sought to review the characteristics, strengths, weaknesses variants,
applications areas and data types applied on the various Dimension Reduction techniques …

Spacecraft faces various situations when carrying out exploration missions in complex
space, thus monitoring the anomaly status of spacecraft is crucial to the development of the …