Multivariate time series (MTS) data are ubiquitous in science and daily life, and how to measure their similarity is a core part of MTS analyzing process. Many of the research efforts in this context have focused on proposing novel similarity measures for the underlying data. However, with the countless techniques to estimate similarity between MTS, this field suffers from a lack of comparative...

Real-world applications often involve irregular time series, for which the intervals between successive observations are non-uniform. Irregularity across multiple features in a multi-variate series further results different subset of at any given (i.e., asynchronicity). Existing pre-training schemes time-series, however, assume regularity and make no special treatment irregularity. We argue tha...

Multivariate Time Series (MTS) forecasting is an essential problem in many fields. Accurate results can effectively help making decisions. To date, MTS methods have been proposed and widely applied. However, these assume that the predicted value of a single variable affected by all other variables, ignoring causal relationship among variables. address above issue, we propose novel end-to-end de...