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Paper Citation Record · LEDGER

Scaling Law for Time Series Forecasting

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2405.15124.

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

pith.paper-citation-record.v1
2405.15124 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:05:56.181289Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:38:43.387910Z

Reference resolution

0 of 0 outbound references displayed

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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 9e3a1ace-9738-4259-a3bd-2584b66f4b24 · inbound

How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models? cites this paper.

How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models? Scaling Law for Time Series Forecasting

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-10T17:09:19.988190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:09:19.988190Z digest=sha256:a4031bbe88394dd2d39474ee4dcb82913c05b85a5ecfa4580cb51b4a2fddf80b

Observation c65734e3-4e80-4cf7-a32b-708e31bc4207 · inbound

Sundial: A Family of Highly Capable Time Series Foundation Models cites this paper.

Sundial: A Family of Highly Capable Time Series Foundation Models Scaling Law for Time Series Forecasting

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-23T04:32:33.846985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-23T04:28:55.141475Z digest=sha256:3387e489b192bdb2786a2ba06f08c362664acbedd5fb0bca3a21ff0e70fa07eb

Observation 7121251a-0aea-4b0c-a5a9-b18ef18204d8 · inbound

Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering cites this paper.

Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering Scaling Law for Time Series Forecasting

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-15T23:05:56.181289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:05:56.181289Z digest=sha256:1f11a7831287dd54ea2c72ad6c263b1f73ddc03f16179678046e7d8944380330

Observation 14aa9d82-22ec-4ace-84fc-76a41bad5202 · inbound

TAB: Unified Benchmarking of Time Series Anomaly Detection Methods cites this paper.

TAB: Unified Benchmarking of Time Series Anomaly Detection Methods Scaling Law for Time Series Forecasting

Reference 90

Resolution
unresolved
no resolver link, observed 2026-08-15T19:00:22.169734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:00:22.169734Z digest=sha256:4ce5a458917c53963e243d700b3ffa0b794ea1ece9e8499c6fdbbec59003bf30

Observation 8017b6d5-4a23-4223-b765-7721fabc6598 · inbound

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting cites this paper.

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting Scaling Law for Time Series Forecasting

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-18T12:51:23.418882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-18T12:49:02.077485Z digest=sha256:f00467325d5df2e97967385279cbbb095299c1b5aa65a8050f8d025ba8fa9583

Observation 2a419e13-f64d-46a1-9278-d76f301f5bbe · inbound

Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling cites this paper.

Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling Scaling Law for Time Series Forecasting

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-15T16:50:10.891840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-15T16:49:19.112440Z digest=sha256:858efff98f86c4c81ea659109eb1176148e96addd83cb3798ab41fe2bf247d57

Observation 977ee58b-7446-4aec-b365-6d24862e149a · inbound

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis cites this paper.

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis Scaling Law for Time Series Forecasting

Reference 172

Resolution
verified exact
arxiv_id, observed 2026-07-03T17:38:43.389273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-07-03T17:34:37.552706Z digest=sha256:a3b7955c93b2662797fc1c97f5d47f5ecb65a0efd6d0e23c4c91a1ffea7d0134