Paper Citation Record · LEDGER

Time Series Data Augmentation for Deep Learning: A Survey

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2002.12478.

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

pith.paper-citation-record.v1
2002.12478 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:18:31.849965Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T18:55:00.450642Z

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 7ce23236-dd3d-4cf4-a6db-e0d28d98a2db · inbound

Spatiotemporal deep learning models for detection of rapid intensification in cyclones cites this paper.

Spatiotemporal deep learning models for detection of rapid intensification in cyclones Time Series Data Augmentation for Deep Learning: A Survey

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:31.849965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32c3abbd-b517-48a5-973f-7684d38959d1 · inbound

Time Series Foundation Models for Multivariate Financial Time Series Forecasting cites this paper.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Time Series Data Augmentation for Deep Learning: A Survey

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T18:49:22.032092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa3a6f43-9cf3-422a-ba78-7d5df4e4f555 · inbound

Discrepancy-Aware Contrastive Adaptation in Medical Time Series Analysis cites this paper.

Discrepancy-Aware Contrastive Adaptation in Medical Time Series Analysis Time Series Data Augmentation for Deep Learning: A Survey

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T23:23:34.430098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2c61f15-b97c-459d-9935-a5e59b2ac4ee · inbound

Text Reinforcement for Multimodal Time Series Forecasting cites this paper.

Text Reinforcement for Multimodal Time Series Forecasting Time Series Data Augmentation for Deep Learning: A Survey

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-05T13:25:55.870221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b890825-a637-43fc-b898-5165ec4e5835 · inbound

Distribution-Free Pretraining of Classification Losses via Evolutionary Dynamics cites this paper.

Distribution-Free Pretraining of Classification Losses via Evolutionary Dynamics Time Series Data Augmentation for Deep Learning: A Survey

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T23:31:13.197422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ea8ccc66-5ced-49a0-b8bb-7a14a1100603 · inbound

Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation cites this paper.

Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation Time Series Data Augmentation for Deep Learning: A Survey

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:51:25.380384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f2a34b7d-a5f7-46f5-acd1-cca517c80ccd · inbound

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data cites this paper.

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data Time Series Data Augmentation for Deep Learning: A Survey

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:23:16.811704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 66366c24-3367-4894-b593-e0897211b425 · inbound

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data cites this paper.

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data Time Series Data Augmentation for Deep Learning: A Survey

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:55:00.452452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8a284a3e-94d2-45d5-a22b-82adc0f3e9c9 · inbound

UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction cites this paper.

UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction Time Series Data Augmentation for Deep Learning: A Survey

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:03:17.776444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c57ba665-fb05-41dd-ad74-203025e72528 · inbound

On the Role of Inductive Bias in Time-Series Pretraining: A Case Study in Learning Generalizable Representations for Clinical Time Series cites this paper.

On the Role of Inductive Bias in Time-Series Pretraining: A Case Study in Learning Generalizable Representations for Clinical Time Series Time Series Data Augmentation for Deep Learning: A Survey

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:14:01.030808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.