Learning temporal causal graph structures from multivariate time-series data reveals important dependency relationships between current observations and histories, and provides a ...
Yan Liu 0002, Alexandru Niculescu-Mizil, Aurelie C...
This paper proposes a nonparametric Bayesian method for exploratory data analysis and feature construction in continuous time series. Our method focuses on understanding shared fe...
We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into K clusters and represents each cluster by a simple pr...
In time series analysis, inference about causeeffect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal struc...
— Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predict...