Multivariate time series data suffer from the problem of missing values, which hinders the
application of many analytical methods. To achieve the accurate imputation of these missing …

The increased availability of multivariate time-series asks for the development of suitable
methods able to holistically analyse them. To this aim, we propose a novel flexible method …

Subsequence clustering of time series is an essential task in data mining, and interpreting
the resulting clusters is also crucial since we generally do not have prior knowledge of the …

A new banded time-varying precision matrix estimator is proposed for high-dimensional time
series. The estimator utilizes the modified Cholesky decomposition, and the two factors in …

Multivariate time series usually have complex and time-varying dependencies among
variables. In order to spot changes and interpret temporal dynamics, it is essential to …

Multivariate time series usually have complex and timevarying dependencies among
variables. In order to spot changes and interpret the temporal dynamics, it is essential to …

Given a collection of multidimensional time-series that contains an unknown type and
number of network structures between variables, how efficiently can we find typical patterns …

In the artificial intelligence community there is a growing consensus that real world data is
naturally represented as graphs because they can easily incorporate complexity at several …

Multivariate time series analysis is becoming an integral part of data analysis pipelines.
Understanding the individual time point connections between covariates as well as how …

Real-world phenomena are often not fully measured or completely observable, raising the
so-called missing data problem. As a consequence, the need of developing ad-hoc …