Paper Citation Record · LEDGER

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting

As of 24 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 1 inbound Pith citation observation for arXiv:2601.21726.

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

pith.paper-citation-record.v1
2601.21726 v2

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T06:57:07.205380Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T12:59:50.701441Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T19:01:16.706345Z

Reference resolution

11 of 11 outbound references displayed

verified exact0
verified fuzzy0
unresolved10
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 466f1f38-d909-44c8-aa7b-08a7d0654da6 · outbound

This paper cites Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models.

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T06:57:07.156030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b9c5139-96ac-4857-b6c6-b9783869912d · outbound

This paper cites TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting.

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T06:57:07.191355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fceca937-134e-441f-af5c-021016ddd2df · outbound

This paper cites Not all data are good labels: On the self-supervised labeling for time series forecasting.arXiv preprint arXiv:2502.14704,.

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting Not all data are good labels: On the self-supervised labeling for time series forecasting.arXiv preprint arXiv:2502.14704,

Reference 2002

Resolution: unresolved; no resolver link, observed 2026-08-03T06:57:07.196029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65512d34-f018-4faf-acb7-2ebecc3c21be · outbound

This paper cites Variational Dropout and the Local Reparameterization Trick.

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting Variational Dropout and the Local Reparameterization Trick

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-03T06:57:07.171474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfa67746-689c-4427-8da3-8d2e757a8ca0 · outbound

This paper cites Auto-Encoding Variational Bayes.

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting Auto-Encoding Variational Bayes

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-03T06:57:07.166363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9941b1dd-1962-402e-912e-1e264b71d916 · outbound

This paper cites Dropout: A simple way to prevent neural networks from overfitting.Journal of Machine Learning Research, 15(56):1929–1958,.

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting Dropout: A simple way to prevent neural networks from overfitting.Journal of Machine Learning Research, 15(56):1929–1958,

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T06:57:07.186413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09a5b84f-0321-41fd-80d6-19d76695b77a · outbound

This paper cites small sample, high noise.

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting small sample, high noise

Reference 2020

Resolution: malformed identifier; no resolver link, observed 2026-08-03T06:57:07.205380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa5a4a1e-aa56-4b8c-b20e-c73479af0fe9 · outbound

This paper cites Film: Frequency improved legendre memory model for long-term time series forecasting.

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting Film: Frequency improved legendre memory model for long-term time series forecasting

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T06:57:07.200868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd3c7f87-f4d7-4c73-8b2e-a2bf44574b08 · outbound

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

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T06:57:07.161568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f95e67e-f525-4a3b-9fc5-485fa8831a63 · outbound

This paper cites Bayestsf: Measuring un- certainty estimation in industrial time series forecasting from a bayesian perspective.

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting Bayestsf: Measuring un- certainty estimation in industrial time series forecasting from a bayesian perspective

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T06:57:07.176295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fd1c523-40aa-41ac-9b33-797d6a16a328 · outbound

This paper cites Recurrent Neural Networks for Time Series Forecasting.

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting Recurrent Neural Networks for Time Series Forecasting

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T06:57:07.181421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation fd29ba0c-a0c0-4816-91e5-676417cbd6cc · inbound

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting cites this paper.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-26T02:03:08.640719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.