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Paper Citation Record · LEDGER

T-Rep: Representation Learning for Time Series using Time-Embeddings

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2310.04486.

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

pith.paper-citation-record.v1
2310.04486 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:07:28.200387Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3c06a19f-62cb-40e6-9485-69073d65973d · inbound

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

Universal Time-Series Representation Learning: A Survey T-Rep: Representation Learning for Time Series using Time-Embeddings

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-24T04:28:53.441632Z

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.

source=pdf_text observed=2026-05-24T04:26:45.527625Z digest=sha256:c0f0a725a5e48d3e90f1224f0e390afbb634bf28a64ed574a4731491956e5da9

Observation b02d3140-f9f3-4665-afa1-af49f65848df · inbound

Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach cites this paper.

Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach T-Rep: Representation Learning for Time Series using Time-Embeddings

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T11:07:28.200387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:07:28.200387Z digest=sha256:13f48b8c48aaf8f6c240810d23a66da6c1cbd71c6554db8f0ad3d9377a73f275

Observation ba0e64b7-66ff-4e53-bb8b-3292630407be · inbound

Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworks cites this paper.

Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworks T-Rep: Representation Learning for Time Series using Time-Embeddings

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T00:23:46.230290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:23:46.230290Z digest=sha256:86f9ffb3aed468523725765eea49b48ed1c1e2fdb15d034a76f0056cc1c3194c

Observation adfeca4c-9989-47e4-9a39-10f575fac1e2 · inbound

Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data cites this paper.

Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data T-Rep: Representation Learning for Time Series using Time-Embeddings

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-04T22:44:39.809244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T22:44:39.809244Z digest=sha256:15826709c6d68f7e43eca518bf44dd87a4a65cf0b5161a81d235101333c26c2d

Observation 3884efa7-9e30-4bea-a726-2325ff6ceb24 · inbound

Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection cites this paper.

Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection T-Rep: Representation Learning for Time Series using Time-Embeddings

Reference 197

Resolution
verified exact
arxiv_id, observed 2026-06-26T00:28:42.775402Z

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.

source=arxiv_source observed=2026-06-26T00:27:41.609691Z digest=sha256:dcc1d50c93dcfadc4746b430f7ddca9a6d779a0972a112f559e173cd8117035b