Paper Citation Record · LEDGER

Measuring Time-Series Dataset Similarity using Wasserstein Distance

As of 8 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2507.22189.

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

pith.paper-citation-record.v1
2507.22189 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:02:36.040700Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

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

13 of 13 outbound references displayed

verified exact1
verified fuzzy3
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d1c3fe1f-c566-4960-9c3e-e762703d2288 · outbound

This paper cites Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation.

Measuring Time-Series Dataset Similarity using Wasserstein Distance Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T12:02:36.027448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation def04a8e-628f-48e5-9942-66843a7d172f · outbound

This paper cites Financial time series forecasting with deep learning: A systematic literature review: 2005–2019.Applied soft computing, 90:106181,.

Measuring Time-Series Dataset Similarity using Wasserstein Distance Financial time series forecasting with deep learning: A systematic literature review: 2005–2019.Applied soft computing, 90:106181,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T12:02:36.030501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ba06cd7-7574-47b8-be7b-ca03552059b0 · outbound

This paper cites Research on healthy anomaly detection model based on deep learning from multiple time-series physiological signals.Scientific Programming, 2016(1):5642856,.

Measuring Time-Series Dataset Similarity using Wasserstein Distance Research on healthy anomaly detection model based on deep learning from multiple time-series physiological signals.Scientific Programming, 2016(1):5642856,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:02:36.167052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 43b6af47-f912-4a3f-ac34-531e76455296 · outbound

This paper cites URLhttps://doi.org/10.24963/ijcai.2024/186.

Measuring Time-Series Dataset Similarity using Wasserstein Distance URLhttps://doi.org/10.24963/ijcai.2024/186

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T12:02:36.040700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05f6f3c2-d4c3-4eed-9fad-eee12aaf5450 · outbound

This paper cites MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts.

Measuring Time-Series Dataset Similarity using Wasserstein Distance MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts

Reference 1989

Resolution: unresolved; no resolver link, observed 2026-08-06T12:02:36.021253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b9d8d2b-a866-4d8e-9060-27891eff9c74 · outbound

This paper cites Don't Stop Pretraining: Adapt Language Models to Domains and Tasks.

Measuring Time-Series Dataset Similarity using Wasserstein Distance Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-06T12:02:36.017785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b33fa381-d108-49a6-8426-755678298a4b · outbound

This paper cites Similarity Between Two Stochastic Differential Systems.

Measuring Time-Series Dataset Similarity using Wasserstein Distance Similarity Between Two Stochastic Differential Systems

Reference 2016

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:02:36.084444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 33875c24-169a-449f-8dbf-f28d30f75082 · outbound

This paper cites A decoder-only foundation model for time-series forecasting.

Measuring Time-Series Dataset Similarity using Wasserstein Distance A decoder-only foundation model for time-series forecasting

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T12:02:36.014572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12f238ec-fc46-4eb2-aa2c-d636a2fe9647 · outbound

This paper cites Chronos: Learning the Language of Time Series.

Measuring Time-Series Dataset Similarity using Wasserstein Distance Chronos: Learning the Language of Time Series

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T12:02:36.004446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4da7de9-ded5-4ee2-8443-e69eb139ce05 · outbound

This paper cites GluonTS: Probabilistic Time Series Models in Python.

Measuring Time-Series Dataset Similarity using Wasserstein Distance GluonTS: Probabilistic Time Series Models in Python

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T12:02:35.999836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63948c0b-e7f8-4287-9e27-0e1e6e12c20f · outbound

This paper cites Unitime: A language-empowered unified model for cross-domain time series forecasting.

Measuring Time-Series Dataset Similarity using Wasserstein Distance Unitime: A language-empowered unified model for cross-domain time series forecasting

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:02:36.187668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7fd5109b-0999-4a62-8361-0a1a6c98566d · outbound

This paper cites A similarity measure of gaussian process predictive distributions.

Measuring Time-Series Dataset Similarity using Wasserstein Distance A similarity measure of gaussian process predictive distributions

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:02:36.198622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cd3a2209-4d7f-47b1-9d3f-db665ebbe859 · outbound

This paper cites Statistical optimal transport.

Measuring Time-Series Dataset Similarity using Wasserstein Distance Statistical optimal transport

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T12:02:36.010956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.