Paper Citation Record · LEDGER

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

As of 8 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 3 inbound Pith citation observations for arXiv:2506.05515.

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

pith.paper-citation-record.v1
2506.05515 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:26:16.724559Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T16:27:45.767100Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T16:35:12.732159Z

Reference resolution

19 of 19 outbound references displayed

verified exact2
verified fuzzy5
unresolved9
parse uncertain0
malformed identifier1
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 29ac5364-c82e-4591-8c9d-71916030f04e · outbound

This paper cites an unresolved cited work.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:26:18.984782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2392e214-d797-4818-b5a6-670658596b65 · outbound

This paper cites an unresolved cited work.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:26:18.050293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2b22b066-d006-480b-a940-359035a29f59 · outbound

This paper cites ETS, Trf.TempFlow and Tactis2, columns are in gray because they don’t share the same backbone as the other baselines.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting ETS, Trf.TempFlow and Tactis2, columns are in gray because they don’t share the same backbone as the other baselines

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:26:18.624780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 75b78a53-b4c6-4989-8ecd-142a7654e594 · outbound

This paper cites In this table, the distortion is computed with a variable number of hypothesesKfor each baseline, as in Table 4 of the main paper.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting In this table, the distortion is computed with a variable number of hypothesesKfor each baseline, as in Table 4 of the main paper

Reference 9

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T10:26:18.213116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 23a0659b-05df-41d0-84c4-78e3adb82549 · outbound

This paper cites These series generally display recurrent rush-hour peaks as well as differences between weekdays and weekends.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting These series generally display recurrent rush-hour peaks as well as differences between weekdays and weekends

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:26:18.970535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a397e219-41ce-4875-80a6-c8b20bcc0545 · outbound

This paper cites Here, TimeMCL follows the same experimental setup as in the previous benchmark, except that we used Z-Score normalization (instead of mean scaling) during training.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Here, TimeMCL follows the same experimental setup as in the previous benchmark, except that we used Z-Score normalization (instead of mean scaling) during training

Reference 12

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T10:26:17.051466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f0c5781a-f612-49c1-9ce0-eee22ab39b8a · outbound

This paper cites Inference.We used the official experimental protocol for evaluation in this benchmark (e.g.,(Rasul et al., 2021a)).

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Inference.We used the official experimental protocol for evaluation in this benchmark (e.g.,(Rasul et al., 2021a))

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:26:18.412653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e821046b-aea0-4732-a947-aedbf4733e0d · outbound

This paper cites We observe thatTimeMCL produces smoother predictions compared to other methods and effectively captures different modes in the conditional distribution.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting We observe thatTimeMCL produces smoother predictions compared to other methods and effectively captures different modes in the conditional distribution

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:26:17.869413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9aa28ab2-11cd-40ce-97f6-771ba723fefe · outbound

This paper cites an unresolved cited work.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Unresolved cited work

Reference 64

Resolution: verified exact; raw_fallback, observed 2026-08-07T10:26:17.297167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d1b9560b-83be-4a42-a61e-871fd52a885f · outbound

This paper cites an unresolved cited work.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Unresolved cited work

Reference 1976

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:26:18.954025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d0aad034-de61-4b90-9979-65dddb916c3c · outbound

This paper cites Appendix A contains the proofs of the theoretical results, establishing that TimeMCL can be interpreted as a functional quantizer.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Appendix A contains the proofs of the theoretical results, establishing that TimeMCL can be interpreted as a functional quantizer

Reference 1982

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:26:19.019692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 859c22ac-99a8-47f0-a808-49434e87a16f · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting WaveNet: A Generative Model for Raw Audio

Reference 2005

Resolution: unresolved; no resolver link, observed 2026-08-07T10:26:15.614683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17cb7ebb-6195-4579-ae8b-42637caf965b · outbound

This paper cites Deep Learning for Time Series Forecasting: Tutorial and Literature Survey.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

Reference 2007

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T10:26:17.665874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e7ce6dc0-ba67-4a09-9ec7-fff44ec9c5aa · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Gaussian Error Linear Units (GELUs)

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-07T10:26:15.528426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfcac169-d09d-472e-85e6-01e5c0bf56e4 · outbound

This paper cites The Nystr\"om method for functional quantization with an application to the fractional Brownian motion.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting The Nystr\"om method for functional quantization with an application to the fractional Brownian motion

Reference 2014

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:26:17.504349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f201c24-5a06-4c5d-a0a7-aead95f03395 · outbound

This paper cites an unresolved cited work.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Unresolved cited work

Reference 2015

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:26:19.001378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 84661d3e-f5f8-41c8-8c5d-5ae643f3f9c9 · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T10:26:15.266633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f30e7056-564e-4f09-b608-0069c2f8dd2c · outbound

This paper cites Generating Sequences With Recurrent Neural Networks.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Generating Sequences With Recurrent Neural Networks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T10:26:15.434691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27d62e35-9cdf-4c55-b1ff-9e39548f80ab · outbound

This paper cites an unresolved cited work.

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:26:18.830421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 5aae305a-1816-444c-b026-2862800ef945 · inbound

ReNF: Rethinking the Design of Neural Long-Term Time Series Forecasters cites this paper.

ReNF: Rethinking the Design of Neural Long-Term Time Series Forecasters Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:06:23.910170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 056bc1e7-bb0b-45b4-a607-31297e1bc102 · inbound

Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting cites this paper.

Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:10:21.972854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e35492e8-6739-46bd-a772-4d22274a63d0 · inbound

PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation cites this paper.

PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:35:12.733607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.