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Paper Citation Record · LEDGER

Variable Selection for Comparing High-dimensional Time-Series Data

As of 17 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2412.06870.

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

pith.paper-citation-record.v1
2412.06870 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:44:22.504974Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:03:55.257224Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b1eaaaac-d1f3-43e3-b866-1591f9491e17 · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

Variable Selection for Comparing High-dimensional Time-Series Data Optuna: A next-generation hyperparameter optimization framework

Reference 1

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Observation 3f9cc97c-780b-4866-94f3-e0e3da77cbd5 · outbound

This paper cites SPlisHSPlasH Library.

Variable Selection for Comparing High-dimensional Time-Series Data SPlisHSPlasH Library

Reference 2

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Observation baa7e16f-8714-4880-a1c0-a5b579f793cb · outbound

This paper cites Sliced and Radon Wasser- steinBarycentersofMeasures.

Variable Selection for Comparing High-dimensional Time-Series Data Sliced and Radon Wasser- steinBarycentersofMeasures

Reference 3

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Observation d8a471ce-ffeb-4cfb-aa7b-6eb0b10323cf · outbound

This paper cites Sensitivity analysis when model outputs are functions.Reliability Engineering & System Safety, 91(10-11):1468–1472, 2006.

Variable Selection for Comparing High-dimensional Time-Series Data Sensitivity analysis when model outputs are functions.Reliability Engineering & System Safety, 91(10-11):1468–1472, 2006

Reference 4

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Observation 7cd63878-954a-4eb9-8e88-54aaa09db046 · outbound

This paper cites Representation learning for a generalized, quantitative comparison of complex model outputs.

Variable Selection for Comparing High-dimensional Time-Series Data Representation learning for a generalized, quantitative comparison of complex model outputs

Reference 5

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Observation 0005b470-118e-4b11-8895-dd858595c062 · outbound

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Variable Selection for Comparing High-dimensional Time-Series Data Unresolved cited work

Reference 6

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Observation 962264da-e41c-4fea-a830-42ddbd1eb39f · outbound

This paper cites CRC Press, 1994.

Variable Selection for Comparing High-dimensional Time-Series Data CRC Press, 1994

Reference 7

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Observation 39bc7cca-3d9f-47d9-bbbc-89789dd0b462 · outbound

This paper cites Trafficgen: Learning to generate diverse and realistic traffic scenarios.

Variable Selection for Comparing High-dimensional Time-Series Data Trafficgen: Learning to generate diverse and realistic traffic scenarios

Reference 8

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Source-reported events for the cited work

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Observation 194638b0-3cff-4965-9c62-c96a7c592ab0 · outbound

This paper cites Large sample analysis of the median heuristic.

Variable Selection for Comparing High-dimensional Time-Series Data Large sample analysis of the median heuristic

Reference 9

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Observation 1df8059a-4cc5-4051-98a2-7bddd365546c · outbound

This paper cites Borgwardt, Malte J.

Variable Selection for Comparing High-dimensional Time-Series Data Borgwardt, Malte J

Reference 10

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Observation 08f72a18-36e2-4c01-965e-cea5c6da6176 · outbound

This paper cites Un- supervised change analysis using supervised learning.

Variable Selection for Comparing High-dimensional Time-Series Data Un- supervised change analysis using supervised learning

Reference 11

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Observation cf15c01a-01a6-42e7-9645-9f996ce3188a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Variable Selection for Comparing High-dimensional Time-Series Data Adam: A Method for Stochastic Optimization

Reference 12

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Observation 7228ebb9-f3fe-45e6-9a7c-3236ade2d83b · outbound

This paper cites Mcgraw-hill New York, 5th edition, 2014.

Variable Selection for Comparing High-dimensional Time-Series Data Mcgraw-hill New York, 5th edition, 2014

Reference 13

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Observation d563a1fd-adcf-4aa7-a55c-594e4e3b66e2 · outbound

This paper cites Morepowerfulselectivekerneltestsforfeatureselection.

Variable Selection for Comparing High-dimensional Time-Series Data Morepowerfulselectivekerneltestsforfeatureselection

Reference 14

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Observation a667731d-fb0f-4b1e-9869-34f51ce5c8bc · outbound

This paper cites Microscopictrafficsimulationusingsumo.

Variable Selection for Comparing High-dimensional Time-Series Data Microscopictrafficsimulationusingsumo

Reference 15

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Observation 6140d532-4a24-42bb-9d04-d55c8a389e1c · outbound

This paper cites Revisiting classifier two-sample tests.

Variable Selection for Comparing High-dimensional Time-Series Data Revisiting classifier two-sample tests

Reference 16

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Observation 7c42955d-d6f4-4488-849c-17137c2fdc84 · outbound

This paper cites Digital twin- driven smart manufacturing: Connotation, reference model, applications and research issues.

Variable Selection for Comparing High-dimensional Time-Series Data Digital twin- driven smart manufacturing: Connotation, reference model, applications and research issues

Reference 17

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Observation de10a92e-d5bf-4154-bd18-c2662a13cabf · outbound

This paper cites arXiv preprint arXiv:2311.01537, 2023.

Variable Selection for Comparing High-dimensional Time-Series Data arXiv preprint arXiv:2311.01537, 2023

Reference 18

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Observation d2650d63-9a61-4647-b1bd-e9c62474a5d1 · outbound

This paper cites Principal differences analysis: Interpretable characteriza- tionofdifferencesbetweendistributions.

Variable Selection for Comparing High-dimensional Time-Series Data Principal differences analysis: Interpretable characteriza- tionofdifferencesbetweendistributions

Reference 19

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Observation 80234aed-c162-45b4-a61b-454669aab0bb · outbound

This paper cites PyTorch: an imperative style, high-performance deep learning library.

Variable Selection for Comparing High-dimensional Time-Series Data PyTorch: an imperative style, high-performance deep learning library

Reference 20

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Observation 22e1b234-935e-4efe-a9e8-1d322f892a25 · outbound

This paper cites Guaranteed con- servation of momentum for learning particle-based fluid dynamics.

Variable Selection for Comparing High-dimensional Time-Series Data Guaranteed con- servation of momentum for learning particle-based fluid dynamics

Reference 21

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Observation 6c605127-f399-41bf-98f6-a57be4a63087 · outbound

This paper cites Construction with digital twin information systems.Data-Centric Engineering, 1:e14, 2020.

Variable Selection for Comparing High-dimensional Time-Series Data Construction with digital twin information systems.Data-Centric Engineering, 1:e14, 2020

Reference 22

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Variable Selection for Comparing High-dimensional Time-Series Data Battaglia

Reference 23

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Variable Selection for Comparing High-dimensional Time-Series Data Unresolved cited work

Reference 24

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This paper cites Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alex Smola, and Arthur Gretton.

Variable Selection for Comparing High-dimensional Time-Series Data Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alex Smola, and Arthur Gretton

Reference 25

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This paper cites A method for comparing multivariate time series with different dimensions.PloS One, 8(2):e54201, 2013.

Variable Selection for Comparing High-dimensional Time-Series Data A method for comparing multivariate time series with different dimensions.PloS One, 8(2):e54201, 2013

Reference 26

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Variable Selection for Comparing High-dimensional Time-Series Data Regression shrinkage and selection via the lasso

Reference 27

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This paper cites Lagrangian fluid sim- ulationwithcontinuousconvolutions.

Variable Selection for Comparing High-dimensional Time-Series Data Lagrangian fluid sim- ulationwithcontinuousconvolutions

Reference 28

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This paper cites Springer, 2009.

Variable Selection for Comparing High-dimensional Time-Series Data Springer, 2009

Reference 29

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This paper cites Oliphant, Matt Haberland, Tyler Reddy, David Cour- napeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J.

Variable Selection for Comparing High-dimensional Time-Series Data Oliphant, Matt Haberland, Tyler Reddy, David Cour- napeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J

Reference 30

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Observation 759c2074-62fa-43b8-ae5e-c845068fc3c0 · outbound

This paper cites Variable Selection for Kernel Two-Sample Tests.

Variable Selection for Comparing High-dimensional Time-Series Data Variable Selection for Kernel Two-Sample Tests

Reference 31

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Variable Selection for Comparing High-dimensional Time-Series Data Akerneltwo-sampletestforfunctionaldata

Reference 32

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Source-reported events for the cited work

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Observation e7c7e144-5425-4a27-847a-30e4cf16266f · outbound

This paper cites Post selection inference with incomplete maximum mean discrepancy estimator.

Variable Selection for Comparing High-dimensional Time-Series Data Post selection inference with incomplete maximum mean discrepancy estimator

Reference 33

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This paper cites It stops the optimisation when the objective value does not change significantly for the past100 epochs.

Variable Selection for Comparing High-dimensional Time-Series Data It stops the optimisation when the objective value does not change significantly for the past100 epochs

Reference 35

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Variable Selection for Comparing High-dimensional Time-Series Data training

Reference 36

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Observation 0694e920-2b27-4130-898b-a08bdf23849f · outbound

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Variable Selection for Comparing High-dimensional Time-Series Data Unresolved cited work

Reference 2012

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raw_fallback, observed 2026-08-11T19:44:23.066224Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T19:44:22.436502Z digest=sha256:62b61c0c02ebaa9a1bab39410aa17d544c40801d43dd7098069c134f6d65944a

Pith citing papers

Observation 273af9dc-e4a3-463e-854d-ca00339667b9 · inbound

Post Hoc Inference for Component Attribution in Multivariate Change-Point Detection cites this paper.

Post Hoc Inference for Component Attribution in Multivariate Change-Point Detection Variable Selection for Comparing High-dimensional Time-Series Data

Reference 24

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no resolver link, observed 2026-08-02T01:03:55.257224Z

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