Papers › Latent Processes Identification From Multi-View Time Series

Latent Processes Identification From Multi-View Time Series

14 May 2023arXiv:2305.08164archive 2025-07-28

Zenan Huang, Haobo Wang, Junbo Zhao, Nenggan Zheng

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.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2305.08164")

Code

Syntology Ran 6 of 10 code samples harvested from 1 repository linked to this paper; 4 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong; 1 ran · fixture could not drive it; 3 ran with no contract checked.

By repository: official repository: 10 samples from 1 repository, 6 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

lccurious/MuLTI officialpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

10 samples harvested; 6 ran; 1 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · honoured contract
1ran · our draft was wrong
1ran · fixture could not drive it
3ran
4unverified

Licence: 10 of the 10 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from lccurious/MuLTI. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

Discriminator lccurious/MuLTI/multi/models/multi.py official repository ran · metamorphic tier: invariant fingerprinted no licence file found · pointer only · f1501104ff70fb06 · report
GroupLinearLayer lccurious/MuLTI/multi/models/multi.py official repository ran · metamorphic tier: invariant no licence file found · pointer only · bd4762c0c3103e1d · report
VARTransitionPrior lccurious/MuLTI/multi/models/multi.py official repository ran · metamorphic tier: invariant fingerprinted no licence file found · pointer only · f2cfff7ad188b800 · report
gen_assignment lccurious/MuLTI/multi/models/multi.py official repository ran · honoured contract fingerprinted no licence file found · pointer only · dbfb857c348e6efd · report
gumbel_matching_indices lccurious/MuLTI/multi/models/multi.py official repository ran · fixture could not drive it fingerprinted no licence file found · pointer only · 12acbbc31c4fc36d · report
is_close_to_permutation lccurious/MuLTI/multi/models/multi.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · 3a2d58129c8d4ab0 · report
MuLTI lccurious/MuLTI/multi/models/multi.py official repository unverified no licence file found · pointer only · db8a5dac871430c6 · report
gumbel_matching lccurious/MuLTI/multi/models/multi.py official repository unverified no licence file found · pointer only · deabadb0b6623e49 · report
kaiming_init lccurious/MuLTI/multi/models/multi.py official repository unverified no licence file found · pointer only · d57904eb3d1890be · report
normal_init lccurious/MuLTI/multi/models/multi.py official repository unverified no licence file found · pointer only · 61decb02540393b5 · report

Tasks

Contrastive LearningTime Series

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

Contrastive Learning

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections