Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Scaling-laws for Large Time-series Models
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2405.13867.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:47:03.255459Z
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 9e855d5c-251c-4e1f-bcd4-a5f9c664d79d · inbound
Creating a Cooperative AI Policymaking Platform through Open Source Collaboration cites this paper.
Creating a Cooperative AI Policymaking Platform through Open Source Collaboration Scaling-laws for Large Time-series Models
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T19:21:23.318211Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d5982cbc-c621-4a23-ade6-42c220f77682 · inbound
Investigating Compositional Reasoning in Time Series Foundation Models cites this paper.
Investigating Compositional Reasoning in Time Series Foundation Models Scaling-laws for Large Time-series Models
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T17:06:03.018426Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f5ce3603-1910-4dec-8861-168bb789238f · inbound
Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning cites this paper.
Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Scaling-laws for Large Time-series Models
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:47:03.255459Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-20T06:33:59.587034+00:00.
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 2026-08-20T06:33:59.587034+00:00.
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.
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.