Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2211.02989.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T15:18:22.816573Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T21:26:17.745169Z
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 798dea18-9119-45b9-8609-a344344babd5 · inbound
TINED: GNNs-to-MLPs by Teacher Injection and Dirichlet Energy Distillation cites this paper.
TINED: GNNs-to-MLPs by Teacher Injection and Dirichlet Energy Distillation A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T15:18:22.816573Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5d855dac-fd0b-4303-a4a1-12c0281b7457 · inbound
An Inertial Sequence Learning Framework for Vehicle Speed Estimation via Smartphone IMU cites this paper.
An Inertial Sequence Learning Framework for Vehicle Speed Estimation via Smartphone IMU A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:34:37.836781Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0e57465e-b919-46e1-83d7-43a7213bc2ed · inbound
Ratio-based Loss Functions cites this paper.
Ratio-based Loss Functions A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:26:17.748111Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.