4 papers
Parameter Identification in Autoregressions under Discrete Sampling or Temporal Aggregation
Marko Mlikota
I consider an AR() process that is observed every periods, either as a snapshot (stock variable) or as a sum over the sampling interval (flow variable). Under fairly mild as…
PARAFAC2-based Coupled Matrix and Tensor Factorizations with Constraints
Carla Schenker, Xiulin Wang, David Horner +2
Data fusion models based on Coupled Matrix and Tensor Factorizations (CMTF) have been effective tools for joint analysis of data from multiple sources. While the vast majority of C…
tPARAFAC2: Tracking evolving patterns in (incomplete) temporal data
Christos Chatzis, Carla Schenker, Max Pfeffer +1
Tensor factorizations have been widely used for the task of uncovering patterns in various domains. Often, the input is time-evolving, shifting the goal to tracking the evolution o…
dCMF: Learning interpretable evolving patterns from temporal multiway data
Christos Chatzis, Carla Schenker, Jérémy E. Cohen +1
Multiway datasets are commonly analyzed using unsupervised matrix and tensor factorization methods to reveal underlying patterns. Frequently, such datasets include timestamps and c…