Paper Citation Record · LEDGER

Implet: A Post-hoc Subsequence Explainer for Time Series Models

As of 17 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2505.08748.

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

pith.paper-citation-record.v1
2505.08748 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:50:38.917945Z

measured 49 of 49 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.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

49 of 49 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ab15710d-69d2-41db-bbe3-b1026010dc5f · outbound

This paper cites Inceptiontime: Finding alexnet for time series classification,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Inceptiontime: Finding alexnet for time series classification,

Reference 1

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Observation 7962c282-2f6c-4673-8386-210c99757870 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 2

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Observation 5b3446dc-e376-463e-bd54-3ecd492b419e · outbound

This paper cites Axiomatic attribution for deep networks,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Axiomatic attribution for deep networks,

Reference 3

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Observation cd0efd44-5ba2-4c9d-9332-099db55f4dff · outbound

This paper cites Not Just a Black Box: Learning Important Features Through Propagating Activation Differences.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Not Just a Black Box: Learning Important Features Through Propagating Activation Differences

Reference 4

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Observation a2342989-3ef4-4db7-8b11-4d982abbc850 · outbound

This paper cites Visualizing and understanding convo- lutional networks,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Visualizing and understanding convo- lutional networks,

Reference 5

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Observation d8dbae51-85a5-4c2f-96ac-6cfdf41ad823 · outbound

This paper cites Explaining time series predictions with dynamic masks,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Explaining time series predictions with dynamic masks,

Reference 6

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Observation ccd8c7d9-67b3-4c84-b171-2285edead0d2 · outbound

This paper cites “why should i trust you?.

Implet: A Post-hoc Subsequence Explainer for Time Series Models “why should i trust you?

Reference 7

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Observation bea8d4d0-68df-4d5e-8b6a-c5c410cee1b3 · outbound

This paper cites A unified approach to interpreting model predictions,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models A unified approach to interpreting model predictions,

Reference 8

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Observation c4df1736-f520-4a3d-8ce8-f460e08b903e · outbound

This paper cites Attention is all you need,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Attention is all you need,

Reference 9

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Observation 47bd92ed-0e2e-43c6-ab0d-8677c591dfc0 · outbound

This paper cites Rethinking attention mechanism in time series classification,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Rethinking attention mechanism in time series classification,

Reference 10

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Observation c87e9106-36a9-4e40-989c-7f828127c109 · outbound

This paper cites Attention-based lstm- cnns for time-series classification,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Attention-based lstm- cnns for time-series classification,

Reference 11

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Observation 8650f889-6f56-476a-872e-a32d398300b3 · outbound

This paper cites Time is not enough: Time- frequency based explanation for time-series black-box models,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Time is not enough: Time- frequency based explanation for time-series black-box models,

Reference 12

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Observation a9dc12b7-c964-4b45-b7a5-f59668a4b89d · outbound

This paper cites Explaining time series clas- sifiers through meaningful perturbation and optimisation,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Explaining time series clas- sifiers through meaningful perturbation and optimisation,

Reference 13

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Observation a8043448-fc8f-416e-a3f8-ed145cb44266 · outbound

This paper cites Counterfactual explanations without opening the black box: Automated decisions and the gdpr,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Counterfactual explanations without opening the black box: Automated decisions and the gdpr,

Reference 14

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Observation f07bb4af-0c46-4ab4-bf62-ca236aae6339 · outbound

This paper cites This looks like that: deep learning for interpretable image recognition,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models This looks like that: deep learning for interpretable image recognition,

Reference 15

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Observation 0c29e61a-8fe8-45a6-8912-bedece5a577a · outbound

This paper cites Instance-based counterfactual explanations for time series classification,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Instance-based counterfactual explanations for time series classification,

Reference 16

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Observation 9e6136dc-375f-45f3-b500-fdc2eb79cad4 · outbound

This paper cites Counterfactual ex- planations for multivariate time series,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Counterfactual ex- planations for multivariate time series,

Reference 17

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Observation 3bca60d5-3781-436e-bc9f-4335b19667d7 · outbound

This paper cites Time series shapelets: a novel technique that allows accurate, interpretable and fast classification,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Time series shapelets: a novel technique that allows accurate, interpretable and fast classification,

Reference 18

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Observation 9edc0355-8efe-4b4c-92fd-35c95543e347 · outbound

This paper cites A shapelet transform for time series classification,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models A shapelet transform for time series classification,

Reference 19

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Observation f003070a-84b9-4834-a2e8-c62d070d9cc4 · outbound

This paper cites Early classification of multivariate temporal observations by extraction of interpretable shapelets,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Early classification of multivariate temporal observations by extraction of interpretable shapelets,

Reference 20

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Observation 7d446c9e-8e07-4085-a6c5-6f9a364d954c · outbound

This paper cites Learning dtw-shapelets for time-series classification,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Learning dtw-shapelets for time-series classification,

Reference 21

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Observation 566b55f1-164c-4e5e-83bb-64736d17b7b2 · outbound

This paper cites Learn- ing time-series shapelets,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Learn- ing time-series shapelets,

Reference 22

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Observation df1f6dc4-db12-4fcd-8e7f-610767573aac · outbound

This paper cites Explaining any time series classifier,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Explaining any time series classifier,

Reference 23

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Observation ad03582e-4c1f-4b02-8962-c3eb7691a62a · outbound

This paper cites Glocal Explanations of Expected Goal Models in Soccer.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Glocal Explanations of Expected Goal Models in Soccer

Reference 24

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Observation 54371f4e-6727-47d5-95e6-6482d56864bc · outbound

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Implet: A Post-hoc Subsequence Explainer for Time Series Models VINE: Visualizing Statistical Interactions in Black Box Models

Reference 25

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This paper cites CohEx: A Generalized Framework for Cohort Explanation.

Implet: A Post-hoc Subsequence Explainer for Time Series Models CohEx: A Generalized Framework for Cohort Explanation

Reference 26

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Observation defe4d14-c406-4884-9e30-9f4abd641669 · outbound

This paper cites Repid: Regional effect plots with implicit interaction detection,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Repid: Regional effect plots with implicit interaction detection,

Reference 27

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Observation 014d0103-e9f2-46ff-b0ef-a57bf5769d1f · outbound

This paper cites Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach,

Reference 28

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Observation 24fb6c12-2281-4f64-a47d-2a71dd5f894b · outbound

This paper cites Decomposing Global Feature Effects Based on Feature Interactions.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Decomposing Global Feature Effects Based on Feature Interactions

Reference 29

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Observation fa963375-0c4f-4386-b425-e9754cd723ac · outbound

This paper cites Global Aggregations of Local Explanations for Black Box models.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Global Aggregations of Local Explanations for Black Box models

Reference 30

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This paper cites Generalizing dtw to the multi-dimensional case requires an adaptive approach,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Generalizing dtw to the multi-dimensional case requires an adaptive approach,

Reference 31

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Observation fb67d1c7-906a-409b-87f2-56b5d783580e · outbound

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Implet: A Post-hoc Subsequence Explainer for Time Series Models A global averaging method for dynamic time warping, with applications to clustering,

Reference 32

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Observation 1edb54c4-609a-4696-ba61-73f6f5eac7da · outbound

This paper cites Lstm fully convolu- tional networks for time series classification,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Lstm fully convolu- tional networks for time series classification,

Reference 33

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Observation d47662c5-1ab2-4c89-ad5d-131dbce7e9c2 · outbound

This paper cites Learning important features through propagating activation differences,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Learning important features through propagating activation differences,

Reference 34

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This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 35

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This paper cites tsai - a state-of-the-art deep learning library for time series and sequential data,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models tsai - a state-of-the-art deep learning library for time series and sequential data,

Reference 36

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Implet: A Post-hoc Subsequence Explainer for Time Series Models Captum: A unified and generic model inter- pretability library for pytorch,

Reference 37

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This paper cites pyts: A python package for time series classification,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models pyts: A python package for time series classification,

Reference 38

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Observation 3e807fe7-d291-4426-a9c6-9aa65b8ac3a0 · outbound

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Implet: A Post-hoc Subsequence Explainer for Time Series Models DTAIDistance,

Reference 39

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Observation 897604ce-e85f-4796-970b-23bbf5964c29 · outbound

This paper cites Everything you know about dynamic time warping is wrong,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Everything you know about dynamic time warping is wrong,

Reference 40

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Observation 6af92ab6-5155-4399-8c97-022af45d75cc · outbound

This paper cites Forecasting time series subject to multiple structural breaks,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Forecasting time series subject to multiple structural breaks,

Reference 41

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Implet: A Post-hoc Subsequence Explainer for Time Series Models Does modeling a structural break improve forecast accuracy?

Reference 42

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This paper cites Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series

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Implet: A Post-hoc Subsequence Explainer for Time Series Models Towards a rigorous evaluation of xai methods on time series,

Reference 44

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This paper cites Learning saliency maps to explain deep time series classifiers,.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Learning saliency maps to explain deep time series classifiers,

Reference 45

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Observation 334351ee-bb26-43af-b4ae-81d5a68e17a4 · outbound

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Implet: A Post-hoc Subsequence Explainer for Time Series Models The ucr time series archive,

Reference 46

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Implet: A Post-hoc Subsequence Explainer for Time Series Models Sanity checks for saliency maps,

Reference 47

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Observation aca339d8-273c-453e-9207-21f30dab5daf · outbound

This paper cites Explanation Space: A New Perspective into Time Series Interpretability.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Explanation Space: A New Perspective into Time Series Interpretability

Reference 48

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Observation 8e7ae0d1-e33b-43da-9940-e5d9b5fe4efa · outbound

This paper cites Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR.

Implet: A Post-hoc Subsequence Explainer for Time Series Models Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

Reference 2018

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