Paper Citation Record · LEDGER

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks

As of 23 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2508.21340.

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

pith.paper-citation-record.v1
2508.21340 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:21:13.793801Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T19:44:36.435748Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T19:53:55.907773Z

Reference resolution

45 of 45 outbound references displayed

verified exact0
verified fuzzy33
unresolved12
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a40b37c6-4624-4138-adc9-51382a56c43a · outbound

This paper cites Sensegen: A deep learning architecture for synthetic sensor data generation.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Sensegen: A deep learning architecture for synthetic sensor data generation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:23.951170Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 784554a2-1a8c-41ea-9908-67139ac80de8 · outbound

This paper cites Synthsonic: Fast, probabilistic modeling and synthesis of tabular data.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Synthsonic: Fast, probabilistic modeling and synthesis of tabular data

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:23.536435Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c29eab7e-3313-45fe-8bfe-c1acd8cbac52 · outbound

This paper cites Data synthesis via differentially private markov random fields.Proceedings of the VLDB Endowment, 14(11):2190–2202, 2021.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Data synthesis via differentially private markov random fields.Proceedings of the VLDB Endowment, 14(11):2190–2202, 2021

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T14:21:07.623139Z

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Observation 403cc18f-78ca-4b77-989c-f0dbfa62ea86 · outbound

This paper cites Gan-leaks: A taxonomy of membership inference attacks against generative models.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Gan-leaks: A taxonomy of membership inference attacks against generative models

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:23.246121Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 85a7b054-6d28-45cb-9e16-f44b19ecff36 · outbound

This paper cites Locally differentially private high-dimensional data synthesis.Science China Information Sciences, 66(1):112101, 2023.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Locally differentially private high-dimensional data synthesis.Science China Information Sciences, 66(1):112101, 2023

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:22.957382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fa6e3f14-94d4-4b29-ba24-b98c1174a214 · outbound

This paper cites Em- pirical evaluation of gated recurrent neural networks on sequence modeling.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Em- pirical evaluation of gated recurrent neural networks on sequence modeling

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:22.700589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 655c2ca4-70ab-4384-8bca-cc5b78369158 · outbound

This paper cites TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T14:21:08.095180Z

Source-reported events for the cited work

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Observation 5c6cfe19-4674-4186-a8b6-69b805b07f07 · outbound

This paper cites Adversarial audio synthe- sis.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Adversarial audio synthe- sis

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:22.372314Z

Source-reported events for the cited work

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Observation 5034debf-8254-4cc0-a7ec-3b39b3553479 · outbound

This paper cites Adarnn: Adaptive learning and forecasting of time series.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Adarnn: Adaptive learning and forecasting of time series

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:22.114869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c9652cd4-0894-4423-97ce-8b207074614d · outbound

This paper cites Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T14:21:08.574746Z

Source-reported events for the cited work

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Observation 48aa0a08-d682-46e9-8ec4-82cbaa5838d3 · outbound

This paper cites Relational data synthesis using generative adversarial networks.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Relational data synthesis using generative adversarial networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:21.917494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bbd9d922-78e0-484a-bf19-fc3410fd274f · outbound

This paper cites Generative adversarial nets.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Generative adversarial nets

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:21.554764Z

Source-reported events for the cited work

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Observation 3a596c4c-cba1-4819-87e8-7d5caa167236 · outbound

This paper cites The capacity and robustness trade- off: Revisiting the channel independent strategy for multivariate time series forecasting.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks The capacity and robustness trade- off: Revisiting the channel independent strategy for multivariate time series forecasting

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:21.224416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a9c8dbd1-d8a3-4a9c-b8d3-5e5bcb1125fc · outbound

This paper cites Denoising diffusion probabilistic models.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Denoising diffusion probabilistic models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T14:21:09.291750Z

Source-reported events for the cited work

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Observation 16953755-7629-4855-b895-14c10a44bdd2 · outbound

This paper cites Long short-term memory.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Long short-term memory

Reference 15

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Source-reported events for the cited work

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Observation 7e2e0b49-7394-4853-bc4d-6192f26e78dc · outbound

This paper cites Psa-gan: Progressive self attention gans for synthetic time series.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Psa-gan: Progressive self attention gans for synthetic time series

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:20.922523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 668afd9f-33dc-4574-9416-9829d5dc6d47 · outbound

This paper cites Progressive growing of gans for improved quality, stability, and variation.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Progressive growing of gans for improved quality, stability, and variation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:20.645740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ddfe7de6-85e8-40a4-a4e1-f731408ee631 · outbound

This paper cites Auto-Encoding Variational Bayes.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Auto-Encoding Variational Bayes

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T14:21:10.399920Z

Source-reported events for the cited work

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Observation ebfef3a8-54f8-4e7e-b2ba-c0c5e12ec845 · outbound

This paper cites Modeling long-and short-term temporal patterns with deep neural networks.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Modeling long-and short-term temporal patterns with deep neural networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T14:21:10.564755Z

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Observation ea9dee8c-d580-4f7a-b8a9-92dc709c573e · outbound

This paper cites In- vertible tabular gans: Killing two birds with one stone for tabular data synthesis.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks In- vertible tabular gans: Killing two birds with one stone for tabular data synthesis

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:20.384753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ec4422b3-c3f9-4c6c-b53c-c7d60fc6e465 · outbound

This paper cites Causal recurrent variational autoencoder for medical time series generation.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Causal recurrent variational autoencoder for medical time series generation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:20.014832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 93326426-32ce-4b45-829d-101c4ee2c9dc · outbound

This paper cites A Critical Review of Recurrent Neural Networks for Sequence Learning.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks A Critical Review of Recurrent Neural Networks for Sequence Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T14:21:11.010523Z

Source-reported events for the cited work

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Observation 486f3ce0-5aea-4285-aeeb-abb28ce54d95 · outbound

This paper cites C-RNN-GAN: Continuous recurrent neural networks with adversarial training.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks C-RNN-GAN: Continuous recurrent neural networks with adversarial training

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T14:21:11.121342Z

Source-reported events for the cited work

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Observation 5afa3122-4cb8-4d95-9bdc-238805a98ba7 · outbound

This paper cites A time series is worth 64 words: Long-term forecasting with transformers.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks A time series is worth 64 words: Long-term forecasting with transformers

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T14:21:11.262963Z

Source-reported events for the cited work

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Observation 23116904-ec41-489e-b008-38b2af8e7399 · outbound

This paper cites Data synthesis based on generative adversarial networks.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Data synthesis based on generative adversarial networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:19.503899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8c538b56-71fa-4746-85c3-c4abfd389dbc · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T14:21:11.494752Z

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Observation d6b508c7-c394-4141-9295-984973bf164e · outbound

This paper cites Dae-gan: Dynamic aspect-aware gan for text-to-image synthesis.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Dae-gan: Dynamic aspect-aware gan for text-to-image synthesis

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:19.197008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ccdaa8a0-4613-403d-bd76-a5d8c2007bdd · outbound

This paper cites Scale- former: Iterative multi-scale refining transformers for time series forecasting.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Scale- former: Iterative multi-scale refining transformers for time series forecasting

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:18.954756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fa35e279-3137-4405-9204-e52b0a6e197b · outbound

This paper cites Multi-resolution diffusion models for time series forecasting.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Multi-resolution diffusion models for time series forecasting

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:18.564842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4df43418-503f-4722-9020-e5819889e3f2 · outbound

This paper cites P3gm: Private high-dimensional data release via privacy preserving phased generative model.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks P3gm: Private high-dimensional data release via privacy preserving phased generative model

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:18.224843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 95d012de-a675-4130-8729-1922816db9f0 · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks WaveNet: A Generative Model for Raw Audio

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:17.953528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ce018bc8-7baf-48f2-a6e9-afa01573baff · outbound

This paper cites Visualizing data using t-sne.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Visualizing data using t-sne

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:17.676488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 53e54b18-c51d-4b69-971b-6b627d9111d0 · outbound

This paper cites Diffusion-gan: Training gans with diffusion.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Diffusion-gan: Training gans with diffusion

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:17.333904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1473b997-51d1-40fd-b855-8087e5491d8d · outbound

This paper cites Transformers in time series: a survey.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Transformers in time series: a survey

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:17.067409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7a0e3a3d-a6a4-4b0c-818b-250b8aeedd9e · outbound

This paper cites Autoformer: Decom- position transformers with auto-correlation for long-term series forecasting.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Autoformer: Decom- position transformers with auto-correlation for long-term series forecasting

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:16.785008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b4e1dbb8-8cdc-4649-89dd-434675542125 · outbound

This paper cites Lightweight privacy-preserving gan framework for model training and image synthesis.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Lightweight privacy-preserving gan framework for model training and image synthesis

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:16.525003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b625c135-5d68-4947-b1f1-7c57951ec1e3 · outbound

This paper cites Time-series generative adversarial networks.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Time-series generative adversarial networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:16.274864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3fb7aa83-6fa9-4092-91d4-6181fadfbe27 · outbound

This paper cites Learning to learn the future: Modeling concept drifts in time series prediction.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Learning to learn the future: Modeling concept drifts in time series prediction

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:15.954844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dc5e1c15-c1a7-4b37-be91-6f61d59f7081 · outbound

This paper cites Ds- former: A double sampling transformer for multivariate time series long-term prediction.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Ds- former: A double sampling transformer for multivariate time series long-term prediction

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:15.763476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5ad0f52f-e8ba-46d6-9faa-08b554e2f2e1 · outbound

This paper cites Attributing fake images to gans: Learn- ing and analyzing gan fingerprints.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Attributing fake images to gans: Learn- ing and analyzing gan fingerprints

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:15.444754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 50b8740d-94f3-4106-abdc-7a381236f510 · outbound

This paper cites Diffusion-TS: Interpretable diffusion for general time series generation.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Diffusion-TS: Interpretable diffusion for general time series generation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:15.156346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1f47e02e-61c3-43fd-b258-36f80305a455 · outbound

This paper cites Quality-aware self-training on differentiable synthesis of rare relational data.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Quality-aware self-training on differentiable synthesis of rare relational data

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:14.894839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b07463d5-822b-46d3-a05a-d11de0ea01e1 · outbound

This paper cites Privbayes: Private data release via bayesian networks.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Privbayes: Private data release via bayesian networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:14.576240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f707f2db-075b-4428-9da5-ca3675371c83 · outbound

This paper cites Crossformer: Transformer utilizing cross- dimension dependency for multivariate time series forecasting.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Crossformer: Transformer utilizing cross- dimension dependency for multivariate time series forecasting

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:21:14.315703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 81e92f5d-989f-47fd-8622-d6b9240bfbb1 · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting.

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T14:21:13.793801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 9742d0f8-fa9e-4407-b228-41aa301a6b10 · inbound

TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation cites this paper.

TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:53:55.909439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.