Pith. sign in

Paper Citation Record · LEDGER

Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.04852.

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

pith.paper-citation-record.v1
2402.04852 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:44:46.433107Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

5
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 51b2b5ac-f480-4f3b-a80e-15a34fc4faec · inbound

HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting cites this paper.

HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-08T05:44:46.433107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T05:44:46.433107Z digest=sha256:87c72601bd7155ccb7d568a688b96fcfbaede0940adde06ae61b8cb3b7f656d6

Observation 000e40b8-58c5-4074-a672-4a71b3480b28 · inbound

Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model cites this paper.

Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-07T20:03:07.339147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T20:03:07.339147Z digest=sha256:674a95db3309a8d0404f0ba92d7deff7a35c326fead6e7dd6adc3dc7d200d81b

Observation f8e90ee3-b2d4-4c8f-aa12-178350143605 · inbound

XFMNet: Decoding Cross-Site and Nonstationary Water Patterns via Stepwise Multimodal Fusion for Long-Term Water Quality Forecasting cites this paper.

XFMNet: Decoding Cross-Site and Nonstationary Water Patterns via Stepwise Multimodal Fusion for Long-Term Water Quality Forecasting Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning

Reference 45

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T10:15:45.809921Z

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.

source=arxiv_source observed=2026-08-06T10:15:45.471113Z digest=sha256:8c3c40610cc700263a6d814138a8c197b526ae2df6c6dcaa0f4b2363cada6cb1

Observation b777136c-96a0-403e-83c6-7f427e0db6b8 · inbound

XFMNet: Decoding Cross-Site and Nonstationary Water Patterns via Stepwise Multimodal Fusion for Long-Term Water Quality Forecasting cites this paper.

XFMNet: Decoding Cross-Site and Nonstationary Water Patterns via Stepwise Multimodal Fusion for Long-Term Water Quality Forecasting Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T10:15:45.653869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T10:15:45.653869Z digest=sha256:8bc8c3da8d00219cd69fb24010f4bc148a516ed64bcdb74bdcea981d431fe474

Observation 0f867c85-0562-4ee7-b996-5eb3b90c50fc · inbound

CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting cites this paper.

CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-03T05:00:53.856188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:00:53.856188Z digest=sha256:62e39013dcbb30bdbcaabcd4e6978fed7ed588bac32948c25635cf2e4e4aa6f7