Pith. sign in

Paper Citation Record · LEDGER

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

As of 17 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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:15.885941Z digest=sha256:f10541ae6ae5c480103babf049920bcc33050a3a1660333ead32e369d20355d1

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:16.629706Z digest=sha256:195b343fc123ef8b3cb23284996743486ad430dbbd4bd32cb9924d247e598462

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:16.244508Z digest=sha256:2c6539902b1050cab71ff3c92146c5492a1eea388d7001030b29fe9136e3a7d6

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:16.515208Z digest=sha256:4b6af6818cdea17a439dbb30b6e91d289083aec838812104d8f08202281cbeaa

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:15.985694Z digest=sha256:7b1da22eabe32dd66ff423afccb5115ff956a23e43197f8d82016a4a98bd26dc

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:16.583538Z digest=sha256:11aa02dbf0750b8cee552ae33c90598522ac859389085e134eb7040237a4eb20

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:16.414967Z digest=sha256:5dcbaed2e76f5d4bf0895347eab2ffca4194ea926363fed10b92a2ded09dd721

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:16.724559Z digest=sha256:4c80a2e1a8dd4773c2606a3ac53a806c02ce021ce74538eeb13c722ef4b532f4

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:16.334981Z digest=sha256:84ca1bf674fbb051eed65e0ae7d5cbf7656d4e730cd336bd580caffd0b0a9476

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:16.076046Z digest=sha256:581a24b0b886ef0020c8a4f0190dc70cf61266b0624c187993bbf7a5ec4d5be3

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:15.661435Z digest=sha256:a2fb3c71a2db8abd5e90f0754a4483c99294253885d264330412d19652144d7b

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.

source=pdf_text observed=2026-08-07T10:26:15.614683Z digest=sha256:d239d6f3d744e7ef25475a6f3181f718faeebd8008adf03fa02dce42f32fbb0a

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:15.169687Z digest=sha256:30459feb65a71bb15ed8f98e07ee554b5636141bceeec78ed63d32127a90372a

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.

source=pdf_text observed=2026-08-07T10:26:15.528426Z digest=sha256:25b968e5780aaeb2c39cb7777fea338f9484c1b1ad6a33777e7365cb427ff3ac

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:15.370684Z digest=sha256:8b9fe0def125eea49123e294035170fb780ea868a30455199a008ffb0f6a0e27

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:15.783774Z digest=sha256:d2b8a0bb050882c57ae6e001ee263d258a99e16ec11d8fc23c5062669dee4bba

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.

source=pdf_text observed=2026-08-07T10:26:15.266633Z digest=sha256:57ef7ee8454730b72d9e6e06233dd0a788f2391531a3cfd9a9e7091c35033535

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.

source=pdf_text observed=2026-08-07T10:26:15.434691Z digest=sha256:f2504e3c411e48353d11e03cbd6b8af3e2f14d13604ea3729802ec79ba12363a

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T10:26:16.169666Z digest=sha256:7ca61e7facf0999f24478fe62c3167c3f73ebe4cae6fe1f10918e236da131f62

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-18T13:03:25.032299Z digest=sha256:01b6859d17e4a6ce148253454ad8789d518196ff6efd0604d7e02e731e1f4c69

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T05:09:06.410581Z digest=sha256:aa1dd134ce00b2a57d502dc57ad368b508eb5bc2c04a9a115e557389b7c886ba

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-30T16:27:45.767100Z digest=sha256:acc71bc3362d93fe487a8341960cd989f68a17b6bb605c490831313468d85db1