Paper Citation Record · LEDGER

A Survey of Deep Learning and Foundation Models for Time Series Forecasting

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2401.13912.

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

pith.paper-citation-record.v1
2401.13912 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:40:46.741477Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

23
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4f2b2076-7826-4e1a-a1ec-52ec8206ab46 · inbound

Graph Retention Networks for Dynamic Graphs cites this paper.

Graph Retention Networks for Dynamic Graphs A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:45:46.195686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 58021fc8-3551-4a8f-947a-13c77c1f0559 · inbound

RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting cites this paper.

RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T11:40:46.741477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a1cf860-ca9e-4324-bbfb-0e2b9229ad4e · inbound

ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting cites this paper.

ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T04:36:26.236750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86641115-cf77-438b-90dc-1c9934cbcf59 · inbound

Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting cites this paper.

Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T13:23:09.226605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa00a045-e755-4248-b1c5-723308e8f9f7 · inbound

Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook cites this paper.

Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 105

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:53:08.502612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0bb779a1-305c-45fd-990f-e97a93970bed · inbound

Empirical Assessment of Time-Series Foundation Models For Power System Forecasting Applications cites this paper.

Empirical Assessment of Time-Series Foundation Models For Power System Forecasting Applications A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:11:05.628769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a2332667-dc83-4aa8-b0ae-02af115c1528 · inbound

DeepL\'evy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Series cites this paper.

DeepL\'evy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Series A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:31:23.996924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 81a443a6-c892-4676-bec8-69f90976a707 · inbound

DeepL\'evy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Series cites this paper.

DeepL\'evy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Series A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:12:22.695838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ebd0c077-06fd-477a-8ebb-223625b4e357 · inbound

DeepL\'evy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Series cites this paper.

DeepL\'evy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Series A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:05:01.965956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4db92484-c300-4a69-8d41-390b1a706374 · inbound

Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models cites this paper.

Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-06-28T06:51:44.873374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 94892ea2-36c7-4e6d-b67c-cff6a90b014a · inbound

Koopman operator theory: fundamentals, control, and applications cites this paper.

Koopman operator theory: fundamentals, control, and applications A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 188

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:07:44.907193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e082eb64-dbf8-4594-ad61-4bdcb4f5427e · inbound

Forecasting Realized Volatility with Time Series Foundation Models: A Comparison with Econometric Benchmarks cites this paper.

Forecasting Realized Volatility with Time Series Foundation Models: A Comparison with Econometric Benchmarks A Survey of Deep Learning and Foundation Models for Time Series Forecasting

Reference 117

Resolution: metadata mismatch; local_arxiv, observed 2026-07-07T19:34:06.473926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.