Paper Citation Record · LEDGER

Interpretable Time Series Autoregression for Periodicity Quantification

As of 11 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 2 inbound Pith citation observations for arXiv:2506.22895.

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

pith.paper-citation-record.v1
2506.22895 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:01:28.214542Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:45:03.045734Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T22:43:51.258086Z

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 30cd00b6-cf2a-46b8-85db-bd2bb175ce19 · outbound

This paper cites The variability of seasonality,.

Interpretable Time Series Autoregression for Periodicity Quantification The variability of seasonality,

Reference 1

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Observation 65c510c1-714e-4510-985b-61bf868ce30e · outbound

This paper cites Changes in rainfall seasonality in the tropics,.

Interpretable Time Series Autoregression for Periodicity Quantification Changes in rainfall seasonality in the tropics,

Reference 2

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Observation 9321d1d2-7c6f-4aef-bdb9-1cea840d8f63 · outbound

This paper cites Seasonality and predictability shape temporal species di- versity,.

Interpretable Time Series Autoregression for Periodicity Quantification Seasonality and predictability shape temporal species di- versity,

Reference 3

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This paper cites an unresolved cited work.

Interpretable Time Series Autoregression for Periodicity Quantification Unresolved cited work

Reference 4

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Interpretable Time Series Autoregression for Periodicity Quantification Unresolved cited work

Reference 5

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Observation 9b6740d5-c7fc-439e-a7a7-9b3b25dce434 · outbound

This paper cites Slowly varying regression under sparsity,.

Interpretable Time Series Autoregression for Periodicity Quantification Slowly varying regression under sparsity,

Reference 6

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Observation e17b3bc3-08aa-47bd-82a1-aa00d1811740 · outbound

This paper cites Correlating time series with interpretable convolutional kernels,.

Interpretable Time Series Autoregression for Periodicity Quantification Correlating time series with interpretable convolutional kernels,

Reference 7

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Observation de322b74-95e0-49b8-a2cb-272f76fcb284 · outbound

This paper cites Discovering dynamic patterns from spatiotemporal data with time-varying low-rank autoregression,.

Interpretable Time Series Autoregression for Periodicity Quantification Discovering dynamic patterns from spatiotemporal data with time-varying low-rank autoregression,

Reference 8

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Interpretable Time Series Autoregression for Periodicity Quantification Unresolved cited work

Reference 9

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This paper cites Time series for macroeconomics and finance,.

Interpretable Time Series Autoregression for Periodicity Quantification Time series for macroeconomics and finance,

Reference 10

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Interpretable Time Series Autoregression for Periodicity Quantification Unresolved cited work

Reference 11

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Observation 0ee5b2d5-0c91-4aee-9326-3f4f66e58586 · outbound

This paper cites A tutorial on estimating time-varying vector autoregressive models,.

Interpretable Time Series Autoregression for Periodicity Quantification A tutorial on estimating time-varying vector autoregressive models,

Reference 12

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Observation 9ab1ab4d-1bf5-491a-9f4f-9a8bd3ea78eb · outbound

This paper cites Time- varying autoregression with low-rank tensors,.

Interpretable Time Series Autoregression for Periodicity Quantification Time- varying autoregression with low-rank tensors,

Reference 13

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Observation 3ecea80f-1557-4381-b27b-d735e37b5af4 · outbound

This paper cites Changing dynamics: Time-varying au- toregressive models using generalized additive modeling.

Interpretable Time Series Autoregression for Periodicity Quantification Changing dynamics: Time-varying au- toregressive models using generalized additive modeling

Reference 14

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Observation ced282a4-62e6-4fa9-b4d9-7f4477fff7be · outbound

This paper cites Time varying structural vector autoregressions and monetary policy,.

Interpretable Time Series Autoregression for Periodicity Quantification Time varying structural vector autoregressions and monetary policy,

Reference 15

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Observation f60067ff-36a0-45c2-a91d-ff7974da0c27 · outbound

This paper cites Autoregressive process modeling via the lasso procedure,.

Interpretable Time Series Autoregression for Periodicity Quantification Autoregressive process modeling via the lasso procedure,

Reference 16

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Source-reported events for the cited work

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Observation 39bf1694-405a-4514-bb00-1b9e79db3849 · outbound

This paper cites Regression shrinkage and selection via the lasso,.

Interpretable Time Series Autoregression for Periodicity Quantification Regression shrinkage and selection via the lasso,

Reference 17

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Observation 0c7df4db-5ebc-49e3-b3e7-b17672bffc1f · outbound

This paper cites Sparse vector autoregressive modeling,.

Interpretable Time Series Autoregression for Periodicity Quantification Sparse vector autoregressive modeling,

Reference 18

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Observation 981bacfc-0ad1-4d59-9c62-c723e72a85fb · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynami- cal systems,.

Interpretable Time Series Autoregression for Periodicity Quantification Discovering governing equations from data by sparse identification of nonlinear dynami- cal systems,

Reference 19

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Observation b3ecac02-1cb4-437a-9595-22b553036759 · outbound

This paper cites Learning sparse nonlinear dynam- ics via mixed-integer optimization,.

Interpretable Time Series Autoregression for Periodicity Quantification Learning sparse nonlinear dynam- ics via mixed-integer optimization,

Reference 20

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Observation 5f32e36f-89f3-4312-ae60-6b53ed0ae41e · outbound

This paper cites Okridge: Scalable optimal k-sparse ridge regression,.

Interpretable Time Series Autoregression for Periodicity Quantification Okridge: Scalable optimal k-sparse ridge regression,

Reference 21

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Observation 0d39a49d-bb36-43cd-b5d5-40854418b8b1 · outbound

This paper cites A sparsity-controlled vector autoregressive model,.

Interpretable Time Series Autoregression for Periodicity Quantification A sparsity-controlled vector autoregressive model,

Reference 22

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Observation 4fcf11b8-0184-4cd3-9774-89f974665f2f · outbound

This paper cites Orthogonal matching pursuit: Recursive function approximation with appli- cations to wavelet decomposition,.

Interpretable Time Series Autoregression for Periodicity Quantification Orthogonal matching pursuit: Recursive function approximation with appli- cations to wavelet decomposition,

Reference 23

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This paper cites Cosamp: Iterative signal recovery from incomplete and inaccurate samples,.

Interpretable Time Series Autoregression for Periodicity Quantification Cosamp: Iterative signal recovery from incomplete and inaccurate samples,

Reference 24

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Interpretable Time Series Autoregression for Periodicity Quantification Subspace pursuit for compressive sensing signal reconstruction,

Reference 25

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Observation e2783293-46c3-4db7-bebc-1b334cd3c75e · outbound

This paper cites A simple new approach to variable selection in regression, with application to genetic fine mapping,.

Interpretable Time Series Autoregression for Periodicity Quantification A simple new approach to variable selection in regression, with application to genetic fine mapping,

Reference 26

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Observation d54c8324-73d3-40ca-aea6-3430abc58e08 · outbound

This paper cites Best subset selection via a modern optimization lens,.

Interpretable Time Series Autoregression for Periodicity Quantification Best subset selection via a modern optimization lens,

Reference 27

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Observation 9b87a5fe-8781-4363-8888-da615ea2b750 · outbound

This paper cites Sparse high-dimensional regres- sion,.

Interpretable Time Series Autoregression for Periodicity Quantification Sparse high-dimensional regres- sion,

Reference 28

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This paper cites Sparse regression at scale: Branch-and-bound rooted in first-order optimization,.

Interpretable Time Series Autoregression for Periodicity Quantification Sparse regression at scale: Branch-and-bound rooted in first-order optimization,

Reference 29

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Observation 0f821951-0711-40bd-b25d-33c767c63ed2 · outbound

This paper cites Rank-one convexification for sparse regression,.

Interpretable Time Series Autoregression for Periodicity Quantification Rank-one convexification for sparse regression,

Reference 30

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Observation 08755fb1-487f-40a3-823e-0efc6213b940 · outbound

This paper cites The backbone method for ultra- high dimensional sparse machine learning,.

Interpretable Time Series Autoregression for Periodicity Quantification The backbone method for ultra- high dimensional sparse machine learning,

Reference 31

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This paper cites Sure independence screening for ultrahigh dimensional feature space,.

Interpretable Time Series Autoregression for Periodicity Quantification Sure independence screening for ultrahigh dimensional feature space,

Reference 32

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Observation 859b8392-5eed-4387-8d94-2fa70b24ee07 · outbound

This paper cites Safe screening rules for l0-regression from perspective relaxations,.

Interpretable Time Series Autoregression for Periodicity Quantification Safe screening rules for l0-regression from perspective relaxations,

Reference 33

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This paper cites Daymet: Monthly climate summaries on a 1-km grid for north america, version 4 r1,.

Interpretable Time Series Autoregression for Periodicity Quantification Daymet: Monthly climate summaries on a 1-km grid for north america, version 4 r1,

Reference 34

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Observation f57fc88b-ab7c-46f9-b124-d341a4a9a238 · outbound

This paper cites Dingyi Zhuang is a Ph.D.

Interpretable Time Series Autoregression for Periodicity Quantification Dingyi Zhuang is a Ph.D

Reference 2021

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Pith citing papers

Observation 53d122a9-a401-4fdf-9b04-78710ce167f8 · inbound

Data-Driven Discovery of Mobility Periodicity for Understanding Urban Systems cites this paper.

Data-Driven Discovery of Mobility Periodicity for Understanding Urban Systems Interpretable Time Series Autoregression for Periodicity Quantification

Reference 2016

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Observation e54e9a3b-a964-4c6e-a77b-eb50e51ae368 · inbound

TailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification cites this paper.

TailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification Interpretable Time Series Autoregression for Periodicity Quantification

Reference 46

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