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Paper Citation Record · LEDGER

Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2210.03675.

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

pith.paper-citation-record.v1
2210.03675 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:33:43.992032Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:29:45.007482Z

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 f6f0dd1d-3656-4542-afed-2363562eb0c2 · inbound

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units cites this paper.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-10T04:33:43.992032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T04:33:43.992032Z digest=sha256:0621e52f4021c7e4e36764b15a6aea7c3e5116ce82333190f7fdaaffa252d31b

Observation f300e906-3623-4bf1-af37-714c4f44a5b6 · inbound

Non-stationary Diffusion For Probabilistic Time Series Forecasting cites this paper.

Non-stationary Diffusion For Probabilistic Time Series Forecasting Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-22T16:51:47.975795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-22T16:49:45.303500Z digest=sha256:2d06239c95752fd1cfa14af823fced5417bc3d153786e1082080f1531f662129

Observation aadd6e6b-3866-46b0-8a21-675b5c54d489 · inbound

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems cites this paper.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 61

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unresolved
no resolver link, observed 2026-08-07T15:34:20.564915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:34:20.564915Z digest=sha256:840da249d91c69ddc1df92a1d5503b803b60f80a32b720b053f5b7c91e2a35f7

Observation df068350-de0a-4324-8ebb-41568a7fdba0 · inbound

Wavelet-based Disentangled Adaptive Normalization for Non-stationary Times Series Forecasting cites this paper.

Wavelet-based Disentangled Adaptive Normalization for Non-stationary Times Series Forecasting Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T10:18:07.782327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:18:07.782327Z digest=sha256:a3c78c70776d4f8b1d26f7e0514c1dd1e60bad58574e16beb63db3ae8c141354

Observation f55398fd-ba53-47d7-94b0-6e32c4891d26 · inbound

Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction cites this paper.

Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 80

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:12:58.659701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-14T21:12:25.130529Z digest=sha256:d5881b9b5e8dcc9610319e325c9452f7f685bbe76385e039951f5f116b14fdf2

Observation 6cacad90-83f7-4c40-ae27-ada612d27a65 · inbound

SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting cites this paper.

SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-07-02T20:57:23.409719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-27T20:01:38.433950Z digest=sha256:a50db897d619a27279bb84934e0f0f0629ea706bc12153270a622f57f896f51f

Observation 38545853-974c-4291-b47d-9ab980364e38 · inbound

Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction cites this paper.

Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 72

Resolution
verified exact
arxiv_id, observed 2026-07-04T10:19:47.885627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T08:55:24.311225Z digest=sha256:f9b172735fdad0cf673f660cefef6cee0f192645657e0b73bab0ad64811ff2b1

Observation 96743097-27a4-41bc-a97c-f1b3ca29cc61 · inbound

Learning the Koopman Operator using Attention Free Transformers cites this paper.

Learning the Koopman Operator using Attention Free Transformers Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 62

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T10:29:45.009096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-26T08:49:32.561520Z digest=sha256:f92d25a1401b79bf81ed39fe44112c9f1fdb82ba972a0a59c5ce478f5f63f29d

Observation b75362e5-0751-40b8-b601-b9151b5763f6 · inbound

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

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T17:38:43.382999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-03T17:34:37.552706Z digest=sha256:014cb7a30d4441e05a7dd589cd82b6255bdc7feb6e8f2b584065318cadbe3beb

Observation 6f3b0189-89c6-44cb-8c89-725dcbfb0fca · inbound

A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems cites this paper.

A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 87

Resolution
unresolved
no resolver link, observed 2026-08-02T00:46:19.512140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T00:46:19.512140Z digest=sha256:f690279b6af77c50f56b8651ab4b6547b95979348fb4245e0bca63c327defdbe

Observation a2717d2e-2db9-414a-b730-322d4f34c2b1 · inbound

MetaKoopman: Bayesian Meta-Learning of Koopman Operators for Modeling Structured Dynamics under Distribution Shifts cites this paper.

MetaKoopman: Bayesian Meta-Learning of Koopman Operators for Modeling Structured Dynamics under Distribution Shifts Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-01T00:11:35.076327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:11:35.076327Z digest=sha256:5ef81b451965c6a19d81081937f3e01300c7e10878d3c28ca8f8c95318598e3d