Paper Citation Record · LEDGER

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series

As of 8 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 1 inbound Pith citation observation for arXiv:2506.23596.

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

pith.paper-citation-record.v1
2506.23596 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:43:08.091065Z

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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:35:37.725706Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact1
verified fuzzy5
unresolved5
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d7d0d997-3632-4214-994c-102ee1163b98 · outbound

This paper cites an unresolved cited work.

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:43:09.710143Z

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 2fd7961c-17a2-43c6-90b4-af501460a390 · outbound

This paper cites TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data.

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T21:43:07.550290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db483eb9-15ca-440d-8f12-c0117ec0f04b · outbound

This paper cites Beatgan: Anomalous rhythm detection using adversarially gener- ated time series.

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series Beatgan: Anomalous rhythm detection using adversarially gener- ated time series

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:09.058026Z

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 dc52800a-1048-4d01-ad1b-1441b94f874f · outbound

This paper cites The result implies that driving anomaly probability to be continuous (MSE) rather than discrete (BCE) is better to learn Anomaly-Aware Forecasting Network effectively.

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series The result implies that driving anomaly probability to be continuous (MSE) rather than discrete (BCE) is better to learn Anomaly-Aware Forecasting Network effectively

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:08.609306Z

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 fbfff071-6eed-4ce8-ae83-715f48498594 · outbound

This paper cites Across these baselines, A2P consistently achieved the highest performance.

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series Across these baselines, A2P consistently achieved the highest performance

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:08.457470Z

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 51c87132-5eb0-4015-85a3-9abf1930c4d8 · outbound

This paper cites an unresolved cited work.

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series Unresolved cited work

Reference 256

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T21:43:08.781589Z

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 ed688bb3-c42b-4daf-9661-d134426c4ee7 · outbound

This paper cites Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series.

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T21:43:07.196531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a11cfffd-1faf-46c1-b568-85df060955d7 · outbound

This paper cites When, Where, and What? A Novel Benchmark for Accident Anticipation and Localization with Large Language Models.

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series When, Where, and What? A Novel Benchmark for Accident Anticipation and Localization with Large Language Models

Reference 2019

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:43:08.290956Z

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 d940f9c2-d698-4516-98a3-096dcd92e282 · outbound

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

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T21:43:07.134202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccc7c596-ffe5-4b40-997f-b33c32257d2b · outbound

This paper cites FITS: Modeling Time Series with $10k$ Parameters.

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series FITS: Modeling Time Series with $10k$ Parameters

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T21:43:07.654322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 801a8b8d-4f70-4423-af82-89a16e0bb4f9 · outbound

This paper cites Anomaly prediction: A novel approach with explicit delay and horizon.

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series Anomaly prediction: A novel approach with explicit delay and horizon

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:09.277425Z

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 b0506449-6087-4bee-b40a-b7e34735fbcc · outbound

This paper cites and Ünal, G.

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series and Ünal, G

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:09.546968Z

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.

Pith citing papers

Observation f83915fa-a4be-4edf-8188-732a5c6a1787 · inbound

SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Prediction cites this paper.

SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Prediction When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-03T04:35:37.725706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.