Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2302.00861.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-03T17:34:37.552706Z
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 2026-08-04T06:34:03.388597+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 2026-08-04T06:34:03.388597+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 2026-08-04T06:34:03.388597+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 2026-08-04T06:34:03.388597+00:00.
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 2026-08-04T06:34:03.388597+00:00.