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Paper Citation Record · LEDGER

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2306.10125.

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

pith.paper-citation-record.v1
2306.10125 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:13:45.404517Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T07:25:29.483804Z

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 1487d8d7-a0f0-4e31-b4a2-fdcf5243686f · inbound

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models cites this paper.

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Reference 123

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T16:03:17.103575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-16T16:03:17.015913Z digest=sha256:291575684e5d9fc3dbe3fe29f08ede6638910ea2671a73630ce9192ddf702b2e

Observation 75dd3231-8a7c-4fa8-b3ca-e231e50ab00b · inbound

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

Universal Time-Series Representation Learning: A Survey Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Reference 231

Resolution
verified exact
arxiv_id, observed 2026-05-24T04:28:53.329962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-24T04:26:45.527625Z digest=sha256:419a758d3c4f426fd4f1601d83161cf581f5f28b880101f70ac2f16698e76312

Observation f5328145-e77c-451f-b2f5-c15413069016 · inbound

Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for Time Series Forecasting on Variable Subset cites this paper.

Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for Time Series Forecasting on Variable Subset Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-12T20:13:45.404517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:13:45.404517Z digest=sha256:9fa9eae341e5e2eca2b38a0fb44db39ab61fd7a26e6ffa4c48bbb265f1d427f3

Observation 4da67f5d-d746-4459-b1d2-d0b6cdd0313e · inbound

Information Subtraction: Learning Representations for Conditional Entropy cites this paper.

Information Subtraction: Learning Representations for Conditional Entropy Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-10T22:38:05.971679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:38:05.971679Z digest=sha256:af7ba6b32013bfd384a84d14366c1e4e27a75e4ac31d999f83719d839667cd03

Observation 62b15337-1497-41d8-8985-299c7a68a804 · inbound

HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting cites this paper.

HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T14:53:56.258120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:53:56.258120Z digest=sha256:7e7d96465c0946c6aedf2414b840cc9f9f6d1b7b932a96f316591d3c3db91c01

Observation 4a4dd78f-7433-4c5f-bed7-48d23891bda8 · inbound

Next-Latent Prediction Transformers Learn Compact World Models cites this paper.

Next-Latent Prediction Transformers Learn Compact World Models Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-25T07:25:29.487033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-25T07:21:04.684347Z digest=sha256:d61763f82c3995941b11eca33d947272873d4ce3a555cbf61ae25c83f5debe55

Observation e72676a6-393e-4744-9e0f-370e90fdfcdd · inbound

Next-Latent Prediction Transformers Learn Compact World Models cites this paper.

Next-Latent Prediction Transformers Learn Compact World Models Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-03T23:27:57.555716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:27:57.555716Z digest=sha256:b98ef98d4527a80cf31bd0fa7dd12719ba466375cf6444f7e5ec8aaeb797ecb4

Observation b82e2f55-f9c4-49ab-9826-482dd6bdd0b6 · inbound

Modular Foundation Models for Time-Series Perception in Digital Twins cites this paper.

Modular Foundation Models for Time-Series Perception in Digital Twins Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Reference 125

Resolution
unresolved
no resolver link, observed 2026-07-12T01:22:51.284207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:d27f89f794ea7da45b00bbb2be82102b045bab97c321982c5efce2563a5a75af