  Understanding the dynamics of time series data typically requires identifying
the unique latent factors for data generation, \textit{a.k.a.}, latent
processes identification. Driven by the independent assumption, existing works
have made great progress in handling single-view data. However, it is a
non-trivial problem that extends them to multi-view time series data because of
two main challenges: (i) the complex data structure, such as temporal
dependency, can result in violation of the independent assumption; (ii) the
factors from different views are generally overlapped and are hard to be
aggregated to a complete set. In this work, we propose a novel framework MuLTI
that employs the contrastive learning technique to invert the data generative
process for enhanced identifiability. Additionally, MuLTI integrates a
permutation mechanism that merges corresponding overlapped variables by the
establishment of an optimal transport formula. Extensive experimental results
on synthetic and real-world datasets demonstrate the superiority of our method
in recovering identifiable latent variables on multi-view time series.
