Paper Citation Record · LEDGER

Surrogate Modeling for Explainable Predictive Time Series Corrections

As of 21 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2412.19897.

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

pith.paper-citation-record.v1
2412.19897 v3

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

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measured 43 of 43 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:18:50.501764Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T18:18:51.428447Z

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 8643ab97-9c7f-472a-b2d9-e60b7c797088 · outbound

This paper cites Toward Physics-guided Time Series Embedding.

Surrogate Modeling for Explainable Predictive Time Series Corrections Toward Physics-guided Time Series Embedding

Reference 1

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This paper cites Learning En- tropy as a Learning-Based Information Concept.

Surrogate Modeling for Explainable Predictive Time Series Corrections Learning En- tropy as a Learning-Based Information Concept

Reference 2

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Observation cf3e76fd-bfe8-4a99-b893-fea00dc8f1d1 · outbound

This paper cites A Survey on the Explainability of Supervised Machine Learning.

Surrogate Modeling for Explainable Predictive Time Series Corrections A Survey on the Explainability of Supervised Machine Learning

Reference 3

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This paper cites Machine learning interpretability: A survey on methods and metrics.

Surrogate Modeling for Explainable Predictive Time Series Corrections Machine learning interpretability: A survey on methods and metrics

Reference 4

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Observation f661b655-eb73-4546-971c-33f8014e634d · outbound

This paper cites Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models.

Surrogate Modeling for Explainable Predictive Time Series Corrections Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models

Reference 5

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This paper cites Chaudhuri.

Surrogate Modeling for Explainable Predictive Time Series Corrections Chaudhuri

Reference 6

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Observation 571ed57b-cd18-46a0-8c8f-02230b902a1b · outbound

This paper cites Explainable ar- tificial intelligence: A survey.

Surrogate Modeling for Explainable Predictive Time Series Corrections Explainable ar- tificial intelligence: A survey

Reference 7

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Surrogate Modeling for Explainable Predictive Time Series Corrections Unresolved cited work

Reference 8

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This paper cites KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley Interactions.

Surrogate Modeling for Explainable Predictive Time Series Corrections KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley Interactions

Reference 9

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Observation acf92912-933d-4d66-810b-27373985530e · outbound

This paper cites Explainable artificial intelligence (xai) darpa-baa-16-53.

Surrogate Modeling for Explainable Predictive Time Series Corrections Explainable artificial intelligence (xai) darpa-baa-16-53

Reference 10

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Observation 0ee4cefc-ab79-4450-9e74-d373d714ec37 · outbound

This paper cites Long Short-Term Memory.

Surrogate Modeling for Explainable Predictive Time Series Corrections Long Short-Term Memory

Reference 11

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This paper cites 2019, pp.

Surrogate Modeling for Explainable Predictive Time Series Corrections 2019, pp

Reference 12

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This paper cites A Combinatorial Formula for Powers of 2 × 2 Matrices.

Surrogate Modeling for Explainable Predictive Time Series Corrections A Combinatorial Formula for Powers of 2 × 2 Matrices

Reference 13

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Observation e9956e1f-26de-419a-b946-6d502275dee2 · outbound

This paper cites From local explanations to global understanding with explainable AI for trees.

Surrogate Modeling for Explainable Predictive Time Series Corrections From local explanations to global understanding with explainable AI for trees

Reference 14

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Observation 5671c1df-d57f-4d6e-b4a7-1da7c62f592f · outbound

This paper cites A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions.

Surrogate Modeling for Explainable Predictive Time Series Corrections A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions

Reference 15

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This paper cites Springer Science & Business Media, 2005.

Surrogate Modeling for Explainable Predictive Time Series Corrections Springer Science & Business Media, 2005

Reference 16

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This paper cites SEGAL time series classification — Stable explanations using a generative model and an adaptive weighting method for LIME.

Surrogate Modeling for Explainable Predictive Time Series Corrections SEGAL time series classification — Stable explanations using a generative model and an adaptive weighting method for LIME

Reference 17

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Observation 70d8b07a-5410-40b6-a44b-5bc3f6429d0d · outbound

This paper cites Explainable artificial intelligence: a comprehensive re- view.

Surrogate Modeling for Explainable Predictive Time Series Corrections Explainable artificial intelligence: a comprehensive re- view

Reference 18

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This paper cites Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges.

Surrogate Modeling for Explainable Predictive Time Series Corrections Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges

Reference 19

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Observation a4d24d84-911f-4aa3-a6dd-c1201253b4b8 · outbound

This paper cites Explainability of AI-predictions based on psy- chological profiling.

Surrogate Modeling for Explainable Predictive Time Series Corrections Explainability of AI-predictions based on psy- chological profiling

Reference 20

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Surrogate Modeling for Explainable Predictive Time Series Corrections Explainability of AI-predictions based on psy- chological profiling

Reference 21

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Observation 9590c0c6-44c8-4263-a195-48534b4a06e2 · outbound

This paper cites Evaluation of interpretabil- ity methods for multivariate time series forecasting.

Surrogate Modeling for Explainable Predictive Time Series Corrections Evaluation of interpretabil- ity methods for multivariate time series forecasting

Reference 22

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Observation 7d5ce02a-43fc-43cf-af8c-cbf9b332e201 · outbound

This paper cites ”Why Should I Trust You?.

Surrogate Modeling for Explainable Predictive Time Series Corrections ”Why Should I Trust You?

Reference 23

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This paper cites Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey.

Surrogate Modeling for Explainable Predictive Time Series Corrections Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey

Reference 24

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This paper cites Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities.

Surrogate Modeling for Explainable Predictive Time Series Corrections Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities

Reference 25

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This paper cites Towards a Rigorous Evaluation of Explainability for Multivariate Time Series.

Surrogate Modeling for Explainable Predictive Time Series Corrections Towards a Rigorous Evaluation of Explainability for Multivariate Time Series

Reference 26

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This paper cites TS-MULE: Local Interpretable Model-Agnostic Ex- planations for Time Series Forecast Models.

Surrogate Modeling for Explainable Predictive Time Series Corrections TS-MULE: Local Interpretable Model-Agnostic Ex- planations for Time Series Forecast Models

Reference 27

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This paper cites TS-MULE: Local Interpretable Model-Agnostic Ex- planations for Time Series Forecast Models.

Surrogate Modeling for Explainable Predictive Time Series Corrections TS-MULE: Local Interpretable Model-Agnostic Ex- planations for Time Series Forecast Models

Reference 28

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Observation 4c7d39be-dd91-49b6-9397-a7877747950d · outbound

This paper cites Physics-informed neural networks for modeling physio- logical time series for cuffless blood pressure estimation.

Surrogate Modeling for Explainable Predictive Time Series Corrections Physics-informed neural networks for modeling physio- logical time series for cuffless blood pressure estimation

Reference 29

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This paper cites LIMESegment: Meaningful, Realistic Time Series Explanations.

Surrogate Modeling for Explainable Predictive Time Series Corrections LIMESegment: Meaningful, Realistic Time Series Explanations

Reference 30

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Observation 83cdc278-d6db-427b-b1a0-2c35e2c8ab09 · outbound

This paper cites Explainable AI by BAPC -- Before and After correction Parameter Comparison.

Surrogate Modeling for Explainable Predictive Time Series Corrections Explainable AI by BAPC -- Before and After correction Parameter Comparison

Reference 31

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Observation 8d85153d-50f7-4c94-a47f-cf79a874f7c4 · outbound

This paper cites Explaining prediction models and individual predictions with feature contributions.

Surrogate Modeling for Explainable Predictive Time Series Corrections Explaining prediction models and individual predictions with feature contributions

Reference 32

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This paper cites The Many Shapley Values for Model Explanation.

Surrogate Modeling for Explainable Predictive Time Series Corrections The Many Shapley Values for Model Explanation

Reference 33

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Surrogate Modeling for Explainable Predictive Time Series Corrections Axiomatic attribu- tion for deep networks

Reference 34

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This paper cites R Package: datasets.

Surrogate Modeling for Explainable Predictive Time Series Corrections R Package: datasets

Reference 35

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Surrogate Modeling for Explainable Predictive Time Series Corrections The Fourier Transform

Reference 36

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Surrogate Modeling for Explainable Predictive Time Series Corrections url: https://www.thefouriertransform.com/

Reference 37

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This paper cites Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions.

Surrogate Modeling for Explainable Predictive Time Series Corrections Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

Reference 38

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Surrogate Modeling for Explainable Predictive Time Series Corrections Slightly Disturbed: A Mathematical Approach to Oscilla- tions and Waves

Reference 39

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Observation 75bb1cfc-23d3-48f0-bddb-9e9476207ff6 · outbound

This paper cites OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms.

Surrogate Modeling for Explainable Predictive Time Series Corrections OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms

Reference 40

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Surrogate Modeling for Explainable Predictive Time Series Corrections WindowSHAP: An efficient framework for explaining time-series classi- fiers based on Shapley values

Reference 41

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Surrogate Modeling for Explainable Predictive Time Series Corrections Fuzzy Rule-Based Local Surrogate Models for Black- Box Model Explanation

Reference 42

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Pith citing papers

Observation b40433c4-2dbe-46fe-add0-41bd6806427b · inbound

When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasters cites this paper.

When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasters Surrogate Modeling for Explainable Predictive Time Series Corrections

Reference 7

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