Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.00502.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T22:33:25.638814Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T16:51:48.005953Z
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 5230c7e4-8254-4967-8090-7f8e9b768fc8 · inbound
MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification cites this paper.
MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T22:33:25.638814Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d5814df1-c6f8-4b61-954b-48a2df4abb1b · inbound
Non-stationary Diffusion For Probabilistic Time Series Forecasting cites this paper.
Non-stationary Diffusion For Probabilistic Time Series Forecasting Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T16:51:48.008898Z
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.
Observation 03bea598-88a9-466e-a51f-906dc0b40f2a · inbound
SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies cites this paper.
SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T01:58:29.231652Z
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.