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Paper Citation Record · LEDGER

Scaling-laws for Large Time-series Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2405.13867.

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

pith.paper-citation-record.v1
2405.13867 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:50:23.904462Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:40:07.764274Z

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 a7284959-9895-417f-bf9a-fb4d18ad47ee · inbound

On the Invariance and Generality of Neural Scaling Laws cites this paper.

On the Invariance and Generality of Neural Scaling Laws Scaling-laws for Large Time-series Models

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:15:55.992563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-11T02:34:14.087140Z digest=sha256:3bfdde80b03d22652cd7f047c7516a67f63f9d830917f95886262bef1befaded

Observation 104f8075-73ab-495d-a476-d2e71887fbd3 · inbound

The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction cites this paper.

The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction Scaling-laws for Large Time-series Models

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-04T20:40:07.765908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-25T19:56:18.285143Z digest=sha256:0db96ff396b366eca8529c7c1c82ee6eea305b186eb0a8d4421bad7e25db255c

Observation de221bc5-c4d0-48e4-8644-e7fbc2b01d24 · inbound

When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters cites this paper.

When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters Scaling-laws for Large Time-series Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-07-11T11:28:48.400513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T11:28:48.400513Z digest=sha256:f821af1a6ea134b48eadec9d45092868d713480ede0b795995a4b339b30db76a

Observation 3266b451-762d-4163-9a63-bcab426e6722 · inbound

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data cites this paper.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Scaling-laws for Large Time-series Models

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-02T09:50:23.904462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:50:23.904462Z digest=sha256:3b535936ed7ef37678ab79de6da8b885bb76f0ff63d0d62b8d9229162fbdbe89