Paper Citation Record · LEDGER

Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2305.18803.

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

pith.paper-citation-record.v1
2305.18803 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 2026-08-04T06:34:03.388597+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T17:34:37.552706Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:38:43.356801Z

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 729c071d-e3e8-40b2-956d-84e0e656700d · inbound

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting cites this paper.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.810038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6d269ab3-eb8d-40da-a592-1ea508335c2a · inbound

Deep Time Series Models: A Comprehensive Survey and Benchmark cites this paper.

Deep Time Series Models: A Comprehensive Survey and Benchmark Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors

Reference 99

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:05:51.506357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e03eafe2-1b6c-43dc-8a0d-223168199bb1 · inbound

Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting cites this paper.

Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors

Reference 52

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T04:07:37.199555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 403d70bf-41b6-4766-91fe-5dc2de90f2ac · inbound

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

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T17:38:43.358278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.