Paper Citation Record · LEDGER

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series

As of 11 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2608.07333.

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

pith.paper-citation-record.v1
2608.07333 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

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measured 42 of 42 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 4b17b9e3-57c4-40b0-8b86-094c9092c995 · outbound

This paper cites In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (2020).

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (2020)

Reference 1

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This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 2

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This paper cites In: Advances in Social Networks Analysis and Mining 2013, ASONAM.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: Advances in Social Networks Analysis and Mining 2013, ASONAM

Reference 3

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series ACM Comput

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This paper cites DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning

Reference 5

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This paper cites Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

Reference 6

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This paper cites In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada

Reference 7

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This paper cites In: International Conference on Artificial Intelligence and Statistics, AISTATS.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: International Conference on Artificial Intelligence and Statistics, AISTATS

Reference 8

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This paper cites Reformer: The Efficient Transformer.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series Reformer: The Efficient Transformer

Reference 9

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This paper cites The Annals of Applied Statistics4(1), 94–123 (2010).

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series The Annals of Applied Statistics4(1), 94–123 (2010)

Reference 10

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This paper cites In: Proceedings of the 13th SIAM International Conference on Data Mining.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: Proceedings of the 13th SIAM International Conference on Data Mining

Reference 11

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This paper cites The 41st International ACM SI- GIR Conference on Research & Development in Information Retrieval (2017), https://api.semanticscholar.org/CorpusID:4922476.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series The 41st International ACM SI- GIR Conference on Research & Development in Information Retrieval (2017), https://api.semanticscholar.org/CorpusID:4922476

Reference 12

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This paper cites ACM Transactions on Knowledge Discovery from Data17, 1 – 21 (2021).

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series ACM Transactions on Knowledge Discovery from Data17, 1 – 21 (2021)

Reference 13

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This paper cites In: International Conference on Learning Represen- tations (ICLR ’18) (2018),https://openreview.net/forum?id=SJiHXGWAZ.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: International Conference on Learning Represen- tations (ICLR ’18) (2018),https://openreview.net/forum?id=SJiHXGWAZ

Reference 14

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Observation af3e8b99-cec3-41a3-9999-9ec0a1dd7828 · outbound

This paper cites In: Annual Conference on Neural Information Processing Systems (NeurIPS) 2022, New Orleans, LA, USA.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: Annual Conference on Neural Information Processing Systems (NeurIPS) 2022, New Orleans, LA, USA

Reference 15

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Observation 1b2229e5-bb31-4354-848a-077ceef08f8f · outbound

This paper cites Learning Time-Varying Graphs from Online Data.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series Learning Time-Varying Graphs from Online Data

Reference 16

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This paper cites https://doi.org/10.1088/1367-2630/ac54c9.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series https://doi.org/10.1088/1367-2630/ac54c9

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This paper cites In: The Eleventh International Conference on Learning Representations, ICLR, Kigali, Rwanda (2023),https: //openreview.net/forum?id=Jbdc0vTOcol.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: The Eleventh International Conference on Learning Representations, ICLR, Kigali, Rwanda (2023),https: //openreview.net/forum?id=Jbdc0vTOcol

Reference 18

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Reference 19

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Observation eac4e518-cdaf-4da7-b3ad-eef155802e42 · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence (2020).

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: Proceedings of the AAAI Conference on Artificial Intelligence (2020)

Reference 20

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: 2017 IEEE International Conference on Data Mining, ICDM

Reference 21

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This paper cites Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019

Reference 22

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: 9th International Conference on Learning Representations, ICLR, Virtual Event, Austria, May 3-7 (2021)

Reference 23

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series CIKM ’25, Association for Computing Machinery (2025)

Reference 24

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: The Twelfth In- ternational Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024.https://openreview.net/forum?id=7oLshfEIC2

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series KDD ’20, Association for Computing Machinery, New York, NY, USA

Reference 29

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This paper cites In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI, Macao, China, August 10-16, 2019.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI, Macao, China, August 10-16, 2019

Reference 30

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series IEEE Internet Things J

Reference 31

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification

Reference 32

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective

Reference 33

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series Multi-Scale Context Aggregation by Dilated Convolutions

Reference 34

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This paper cites In: The Thirty-ninth Annual Conference on Neural Information Processing Systems (2025),https://openreview.net/forum?id=DAyKP1tvwI.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: The Thirty-ninth Annual Conference on Neural Information Processing Systems (2025),https://openreview.net/forum?id=DAyKP1tvwI

Reference 35

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This paper cites Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017) Overcoming Temporal Correlation Volatility in GNNs 19.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017) Overcoming Temporal Correlation Volatility in GNNs 19

Reference 37

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: Thirty-Fifth Conference on Artificial Intelligence

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When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series In: Proc

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Pith citing papers

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