Paper Citation Record · LEDGER

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection

As of 18 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 3 inbound Pith citation observations for arXiv:2504.14204.

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

pith.paper-citation-record.v1
2504.14204 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:57:59.959487Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:06:26.023763Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T12:53:05.751686Z

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy26
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 74579d98-8967-41a0-b765-4e014067e995 · outbound

This paper cites Pipeline safety early warning method for distributed signal using bilinear CNN and lightgbm,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Pipeline safety early warning method for distributed signal using bilinear CNN and lightgbm,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.317041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 918f749b-cb84-481e-b578-399e623481c1 · outbound

This paper cites A review on outlier/anomaly detection in time series data,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection A review on outlier/anomaly detection in time series data,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.308207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 287fb0ba-8bb2-4ce0-ab0e-739ca29987d0 · outbound

This paper cites Anomaly detection in uasn localization based on time series analysis and fuzzy logic,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Anomaly detection in uasn localization based on time series analysis and fuzzy logic,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.298764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3ea22338-824f-405d-98f1-540b0f982fb5 · outbound

This paper cites An improved arima-based traffic anomaly detection algorithm for wireless sensor networks,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection An improved arima-based traffic anomaly detection algorithm for wireless sensor networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.290306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e4d8e5e9-640a-4fee-8fe9-afc27b1c13d5 · outbound

This paper cites Stl-convtransformer: Series decomposition and convolution-infused transformer architecture in multivariate time series anomaly detection,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Stl-convtransformer: Series decomposition and convolution-infused transformer architecture in multivariate time series anomaly detection,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.281866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 34fb3370-e5b7-4360-b498-c673f1e757dc · outbound

This paper cites Improved lstm-based time- series anomaly detection in rail transit operation environments,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Improved lstm-based time- series anomaly detection in rail transit operation environments,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.272911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cdcac4a7-9fa9-4cec-9778-86d83f9a34d6 · outbound

This paper cites Attention is all you need,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Attention is all you need,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.264319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f8e35c5f-bed3-4dd8-af97-2f9e7bb91b6a · outbound

This paper cites Anomaly transformer: time series anomaly detection with association discrepancy,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Anomaly transformer: time series anomaly detection with association discrepancy,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.866264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d7a503e-27fa-4585-9536-ec301707b8a2 · outbound

This paper cites Dcdetector: Dual attention contrastive representation learning for time series anomaly detection,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Dcdetector: Dual attention contrastive representation learning for time series anomaly detection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.250430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 95013c00-5fc5-48bc-89ec-c5d773ae875e · outbound

This paper cites Seasonal arma-based spc charts for anomaly detection: Application to emergency department systems,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Seasonal arma-based spc charts for anomaly detection: Application to emergency department systems,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.241860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fdcc0adc-a565-49b5-8a26-39235703016e · outbound

This paper cites Machine learning-assisted improved anomaly detection for structural health monitoring,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Machine learning-assisted improved anomaly detection for structural health monitoring,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.874949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69507b4f-774a-48c0-83b8-a445237777a6 · outbound

This paper cites Clustering- based granular representation of time series with application to collective anomaly detection,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Clustering- based granular representation of time series with application to collective anomaly detection,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.877977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0117b774-f79a-44c5-9faf-e6bbc074de96 · outbound

This paper cites Event-based anomaly detection using a one-class svm for a hybrid electric vehicle,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Event-based anomaly detection using a one-class svm for a hybrid electric vehicle,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.222348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 479d9d18-0c64-4b1c-b7a9-f8f9c8fabf0a · outbound

This paper cites In-situ early anomaly detection and remaining useful lifetime prediction for high-power white leds with distance and entropy-based long short-term memory recurrent neural networks,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection In-situ early anomaly detection and remaining useful lifetime prediction for high-power white leds with distance and entropy-based long short-term memory recurrent neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.213096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d4e2ee23-e279-42ac-82c5-498c11f9db29 · outbound

This paper cites Time series anomaly detection with adversarial reconstruction net- works,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Time series anomaly detection with adversarial reconstruction net- works,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.204163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ecfb1f99-f482-4056-9da4-1f11f0745d34 · outbound

This paper cites Anomaly transformer: Time series anomaly detection with association discrepancy,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Anomaly transformer: Time series anomaly detection with association discrepancy,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.195723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8e8a9904-c408-4134-bdfe-a62ae2d20239 · outbound

This paper cites Class-imbalanced time series anomaly detection method based on cost-sensitive hybrid network,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Class-imbalanced time series anomaly detection method based on cost-sensitive hybrid network,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.186941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d52cf410-b6cb-47ba-bff1-ce677b4dfbec · outbound

This paper cites Grand: Gan-based software runtime anomaly detection method using trace information,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Grand: Gan-based software runtime anomaly detection method using trace information,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.178089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5eca0b93-c346-42e8-9740-83bfa8c6b6af · outbound

This paper cites Paformer: anomaly detection of time series with parallel-attention transformer,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Paformer: anomaly detection of time series with parallel-attention transformer,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.169069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e51ca571-f5b7-41bb-81bc-152d91f6d8c1 · outbound

This paper cites Transformer-based multivariate time se- ries anomaly detection using inter-variable attention mechanism,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Transformer-based multivariate time se- ries anomaly detection using inter-variable attention mechanism,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.159589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dd10481a-cb5d-43f6-85ad-5c8c9e747c27 · outbound

This paper cites Imdiffusion: Imputed diffusion models for multivariate time series anomaly detection,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Imdiffusion: Imputed diffusion models for multivariate time series anomaly detection,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.150408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ff911275-5c33-4b6a-a4a7-6775d1f4638c · outbound

This paper cites Multivariate time series anomaly detection via dynamic graph attention network and informer,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Multivariate time series anomaly detection via dynamic graph attention network and informer,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.140624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8896634d-0a1c-43c0-b7ec-a5b417d30411 · outbound

This paper cites Itrans- former: Inverted transformers are effective for time series forecasting,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Itrans- former: Inverted transformers are effective for time series forecasting,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.131570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d9995236-60c6-4e76-8924-d15c41d186be · outbound

This paper cites Drift doesn’t matter: dynamic decomposition with diffusion recon- struction for unstable multivariate time series anomaly detection,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Drift doesn’t matter: dynamic decomposition with diffusion recon- struction for unstable multivariate time series anomaly detection,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.122308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 98e69426-94c5-413c-859f-aad5bc2d18fc · outbound

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

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Informer: Beyond efficient transformer for long sequence time-series forecasting,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.915164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82d62d22-4bc2-4284-84b0-d7c88fe6605d · outbound

This paper cites Exploring simple siamese representation learning,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Exploring simple siamese representation learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.107720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4eaf8fa7-0988-4224-ab08-d577f795313b · outbound

This paper cites A unifying review of deep and shallow anomaly detection,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection A unifying review of deep and shallow anomaly detection,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.098393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0e7a2f44-83a7-4a1e-b425-b2e6eca4a95f · outbound

This paper cites Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.923565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f4f6e5f-2ed5-48f8-aa3d-0d2c772cd1ec · outbound

This paper cites Practical approach to asyn- chronous multivariate time series anomaly detection and localization,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Practical approach to asyn- chronous multivariate time series anomaly detection and localization,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.926713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f952a9b3-3fc6-48f7-b831-ad1fb82f215c · outbound

This paper cites Swat: A water treatment testbed for research and training on ics security,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Swat: A water treatment testbed for research and training on ics security,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.932449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73d33eb1-7e4f-4c0f-9f08-dcbf37381ad6 · outbound

This paper cites Lof: Identifying density- based local outliers,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Lof: Identifying density- based local outliers,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.063230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a0aaf2ed-586f-45a3-b6b7-57baf4243df3 · outbound

This paper cites Robust anomaly detection for multivariate time series through stochastic recurrent neural network,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Robust anomaly detection for multivariate time series through stochastic recurrent neural network,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.938464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3123560e-6dba-4e59-9300-5a9d2ecad620 · outbound

This paper cites Multivariate time series anomaly detection and interpretation using hierarchical inter- metric and temporal embedding,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Multivariate time series anomaly detection and interpretation using hierarchical inter- metric and temporal embedding,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.941548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10a7dcf3-e48b-4f0e-8a81-93be9772aedd · outbound

This paper cites Time series change point detection with self-supervised contrastive predictive coding,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Time series change point detection with self-supervised contrastive predictive coding,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:58:00.040285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ff5a116b-5b29-4d05-abaf-9225aec197a4 · outbound

This paper cites Tranad: Deep transformer networks for anomaly detection in multivariate time series data,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Tranad: Deep transformer networks for anomaly detection in multivariate time series data,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.947423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d53bb16f-6f4b-4908-8362-2eb72fe914a0 · outbound

This paper cites Memory-augmented u-transformer for multivariate time series anomaly detection,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Memory-augmented u-transformer for multivariate time series anomaly detection,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.950631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 887ceabb-7f9b-41be-8665-4bfeb2405dda · outbound

This paper cites An adversarial time- frequency reconstruction network for unsupervised anomaly detection,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection An adversarial time- frequency reconstruction network for unsupervised anomaly detection,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.953688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c7dc7ba-0eb8-4e45-905f-be6d0acc7b38 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection A simple framework for contrastive learning of visual representations,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:59.994943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ef8ed71-a518-41b9-a891-0cba4644a315 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection Momentum contrast for unsupervised visual representation learning,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:59.959487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 312289f3-2b86-4d2e-b088-1a228bbe9ed7 · inbound

Butter: Frequency Consistency and Hierarchical Fusion for Autonomous Driving Object Detection cites this paper.

Butter: Frequency Consistency and Hierarchical Fusion for Autonomous Driving Object Detection DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T18:06:26.023763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee0b85d5-b6e9-4a5a-8acb-0adbbd10a28c · inbound

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers cites this paper.

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:53:05.756396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation da27d347-3885-4dd5-9b48-f804b398c0f6 · inbound

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture cites this paper.

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T12:53:14.963527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.