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Paper Citation Record · LEDGER

A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2302.02173.

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

pith.paper-citation-record.v1
2302.02173 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:20:32.030037Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T23:05:51.518653Z

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 9f6d174a-b84b-4618-8002-d5db43042141 · inbound

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

Deep Time Series Models: A Comprehensive Survey and Benchmark A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

Reference 81

Resolution
verified exact
arxiv_id, observed 2026-05-23T23:05:51.521362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-23T23:03:45.096751Z digest=sha256:3e7797735b1ddd06979f7c544bed1f2f0b517e7dfd08221c37ad80b434fc4cba

Observation 0886f6f9-bbd3-4eb0-b39c-02a3763d1233 · inbound

Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting cites this paper.

Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-12T14:01:09.401956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:01:09.401956Z digest=sha256:b52cd3f936b7a297c9a67e04ee393134594468ec6b5443371aed4fe4759bc4e4

Observation 1cd10e66-f2a5-4914-9921-e8324c42fda5 · inbound

FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting cites this paper.

FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-10T15:58:14.774503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T15:58:14.774503Z digest=sha256:985403a7d3f248b2e3a47c11ae3250f8877e497bf0dd3d4e6647c1721ca4b3e7

Observation e1a8d3e9-f3bd-4d28-854c-a0b42d7ea8ef · inbound

Fourier Asymmetric Attention on Domain Generalization for Pan-Cancer Drug Response Prediction cites this paper.

Fourier Asymmetric Attention on Domain Generalization for Pan-Cancer Drug Response Prediction A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-08T23:53:09.103011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T23:53:09.103011Z digest=sha256:a92b39609f8add72815ba1e7d23b03bba6ae8347579fbe47e2641ac0ef78b1a7

Observation d68b1798-6474-44a9-b6b0-043221152443 · 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 A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-08T22:33:25.758382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T22:33:25.758382Z digest=sha256:b136571bef0d6995e80a1d2bc3684f052d57e079b83bb97cbf85d203cf88187a

Observation 99600e60-6473-4616-8ee6-a6742ad1c890 · inbound

General Transform: A Unified Framework for Adaptive Transform to Enhance Representations cites this paper.

General Transform: A Unified Framework for Adaptive Transform to Enhance Representations A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-15T23:20:32.030037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:20:32.030037Z digest=sha256:01b71259865793cb62c5b57423e9e696053bf2cbe50e7ed2229e2f33f7462765

Observation 211c25de-6922-463c-bcdf-6673233380a3 · inbound

Spectral methods: crucial for machine learning, natural for quantum computers? cites this paper.

Spectral methods: crucial for machine learning, natural for quantum computers? A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

Reference 82

Resolution
verified exact
arxiv_id, observed 2026-05-15T00:23:22.327969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-15T00:22:54.089145Z digest=sha256:05c2e1ab7ab2e2fa4146ce8f2ae30cc91dcd9a562eda20ec27e249459e8fbad1

Observation 4e21f9f2-97ac-4b55-975a-70d4f20aea99 · inbound

GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery cites this paper.

GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

Reference 86

Resolution
verified exact
arxiv_id, observed 2026-05-22T07:44:42.788723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-22T07:42:29.916098Z digest=sha256:d1d1d702ad8512184462189f3cd17505799f7a8c9760f5c7957a2953f0c6e352