Paper Citation Record · LEDGER

Chaos as an interpretable benchmark for forecasting and data-driven modelling

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2110.05266.

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

pith.paper-citation-record.v1
2110.05266 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:38:04.005383Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T13:41:36.160100Z

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 b10ae8a6-ee49-456f-b900-31e046dc3f3e · inbound

Optimizing Hard Thresholding for Sparse Model Discovery cites this paper.

Optimizing Hard Thresholding for Sparse Model Discovery Chaos as an interpretable benchmark for forecasting and data-driven modelling

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-16T05:38:04.005383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97ccc323-0e49-4bce-b905-ad86823954b5 · inbound

FlowMixer: A Depth-Agnostic Neural Architecture for Interpretable Spatiotemporal Forecasting cites this paper.

FlowMixer: A Depth-Agnostic Neural Architecture for Interpretable Spatiotemporal Forecasting Chaos as an interpretable benchmark for forecasting and data-driven modelling

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:41:36.164079Z

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.

Observation 2f50e34e-a4cc-4886-afe6-442a511f09db · inbound

A tensor network approach for chaotic time series prediction cites this paper.

A tensor network approach for chaotic time series prediction Chaos as an interpretable benchmark for forecasting and data-driven modelling

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:24.861046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 991053cf-a667-4956-bdba-e7319f4f5e82 · inbound

Sparse Identification of Nonlinear Dynamics with Conformal Prediction cites this paper.

Sparse Identification of Nonlinear Dynamics with Conformal Prediction Chaos as an interpretable benchmark for forecasting and data-driven modelling

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T17:07:48.944831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ae16cca-d8fc-4c37-b1eb-4bcc7dbf3744 · inbound

Learning with Mandelbrot and Julia cites this paper.

Learning with Mandelbrot and Julia Chaos as an interpretable benchmark for forecasting and data-driven modelling

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T13:16:02.535689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6da89b23-3fc7-4cb9-a2b1-f0bea37f3a4a · inbound

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series cites this paper.

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series Chaos as an interpretable benchmark for forecasting and data-driven modelling

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:05:31.374798Z

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.

Observation 47774e12-2403-4b96-b46f-20c62bcb17f6 · inbound

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series cites this paper.

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series Chaos as an interpretable benchmark for forecasting and data-driven modelling

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T23:18:18.862038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 187b51ee-05e7-4179-b31b-cfeba6649d0a · inbound

Fourier Weak SINDy: Spectral Test Function Selection for Robust Model Identification cites this paper.

Fourier Weak SINDy: Spectral Test Function Selection for Robust Model Identification Chaos as an interpretable benchmark for forecasting and data-driven modelling

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T15:50:00.751983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e0d4e44-a9ab-44e5-998f-230eaa0b8359 · inbound

Attractor Geometry Determines the Identifiability Limits of System Discovery cites this paper.

Attractor Geometry Determines the Identifiability Limits of System Discovery Chaos as an interpretable benchmark for forecasting and data-driven modelling

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T15:24:51.720122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.