Paper Citation Record · LEDGER

Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2401.17548.

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

pith.paper-citation-record.v1
2401.17548 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:01:55.860948Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T12:28:16.783923Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2a899acc-13ca-456e-a37b-625848c4d747 · inbound

Causal Time-Series Synchronization for Multi-Dimensional Forecasting cites this paper.

Causal Time-Series Synchronization for Multi-Dimensional Forecasting Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T20:01:55.860948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fce7f5a9-77fa-41cf-8b3b-55f5b53874e0 · inbound

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting cites this paper.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:19.670288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b097fd10-f5e4-422e-b503-92d3ee8e6d2e · inbound

Dynamic Relational Priming Improves Transformer in Multivariate Time Series cites this paper.

Dynamic Relational Priming Improves Transformer in Multivariate Time Series Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T16:45:00.507812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80d809ed-7b9c-4db8-8e91-5a368a95b55c · inbound

L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting cites this paper.

L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:28:16.785520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.