Paper Citation Record · LEDGER

SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2302.00861.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2302.00861 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:50:51.663489Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T17:38:43.258076Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 042d53cc-b502-4262-bccd-42f68cb3ef6a · inbound

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting cites this paper.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.789374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation edd6e867-916a-4445-819e-2736a56e5548 · inbound

Universal Time-Series Representation Learning: A Survey cites this paper.

Universal Time-Series Representation Learning: A Survey SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-24T04:28:53.358492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation be54b4fe-fe87-4e03-a8c1-62fde4914db9 · inbound

Chronos: Learning the Language of Time Series cites this paper.

Chronos: Learning the Language of Time Series SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-13T08:27:23.405977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a24aab7e-01ab-4b92-874d-241ae1cc1e7a · inbound

Deep Time Series Models: A Comprehensive Survey and Benchmark cites this paper.

Deep Time Series Models: A Comprehensive Survey and Benchmark SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

Reference 194

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:05:51.511167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 13a721dc-16fa-42b7-becd-8dcd037924e3 · inbound

eMargin: Revisiting Contrastive Learning with Margin-Based Separation cites this paper.

eMargin: Revisiting Contrastive Learning with Margin-Based Separation SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T15:50:51.663489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a85275c-12be-4643-a85a-cb83da22ade4 · inbound

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis cites this paper.

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

Reference 67

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T17:38:43.259581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.