Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1905.03806.
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 2026-08-14T05:52:12.483854Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-04T21:06:24.951100Z
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 dac15eb6-647d-422a-89b4-7922115c5e98 · inbound
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions cites this paper.
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
Reference 76
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:52:12.483854Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dc6a9714-d7ab-44b8-a1d1-ae32bc578cd6 · inbound
Comparative Analysis of Global and Local Probabilistic Time Series Forecasting for Contiguous Spatial Demand Regions cites this paper.
Comparative Analysis of Global and Local Probabilistic Time Series Forecasting for Contiguous Spatial Demand Regions Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
Reference 36
- Resolution
- verified exact
- local_arxiv, observed 2026-08-04T21:06:24.989979Z
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.