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Paper Citation Record · LEDGER

Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.10060.

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

pith.paper-citation-record.v1
2310.10060 v7

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T04:24:04.181960Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

4
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8ac3e99a-4390-4d6b-ac95-e0df23470973 · inbound

Swarm Characteristic Classification using Robust Neural Networks with Optimized Controllable Inputs cites this paper.

Swarm Characteristic Classification using Robust Neural Networks with Optimized Controllable Inputs Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-09T04:24:04.181960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T04:24:04.181960Z digest=sha256:c706be9f4849d64ff2c7fcb899ad2f67090ef58d92245f72c6cbf7a9a2e7bc3f

Observation 5b95d2c5-6784-46cd-a97e-dc15235c681f · inbound

A Joint Learning Framework with Feature Reconstruction and Prediction for Incomplete Satellite Image Time Series in Agricultural Semantic Segmentation cites this paper.

A Joint Learning Framework with Feature Reconstruction and Prediction for Incomplete Satellite Image Time Series in Agricultural Semantic Segmentation Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey

Reference 31

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:22:47.329460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-07T14:22:43.949261Z digest=sha256:f86a2969dc45a629a034202032d0edecd847a6154f1c2a630ad7e1fccbc25c58