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Paper Citation Record · LEDGER

CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2409.18479.

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

pith.paper-citation-record.v1
2409.18479 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:04:13.539446Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T10:57:05.472163Z

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 a5a1e9c7-a8cf-4085-a3e8-dc283cfe86da · inbound

A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series Forecasting cites this paper.

A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series Forecasting CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T12:04:13.539446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:04:13.539446Z digest=sha256:f6edbc4b1b2e1c8b6f4eb8eb612059e30e16641afecb3992d9e91619e9d6e4ab

Observation d464a0aa-2f7a-4cf4-923c-85d32e95bb51 · inbound

LightGTS: A Lightweight General Time Series Forecasting Model cites this paper.

LightGTS: A Lightweight General Time Series Forecasting Model CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-07T06:08:56.451133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:08:56.451133Z digest=sha256:6a25fbebc13b7d59c73f2d3a770613e673cfe8dc1aee34dca6aa1d7ffb701def

Observation 541c96a2-fef8-428f-a874-b8d9cd705578 · inbound

RhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting cites this paper.

RhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-07-10T10:57:05.473528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-10T10:54:22.996938Z digest=sha256:a8884a8a88fc925fd11eddb2603b8ba09e53d09a55392f7d01513de3910504e0