Paper Citation Record · LEDGER

Timer: Generative Pre-trained Transformers Are Large Time Series Models

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2402.02368.

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

pith.paper-citation-record.v1
2402.02368 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:15:44.767231Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:19:50.478446Z

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 ef481ea8-aefb-4f68-a3fc-98d48ef4a0a4 · inbound

Deep Time Series Models: A Comprehensive Survey and Benchmark cites this paper.

Deep Time Series Models: A Comprehensive Survey and Benchmark Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 155

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:05:51.453718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4c3b9edc-43f7-4e1e-b413-6170ed3d6ae4 · inbound

Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting cites this paper.

Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:25:34.132017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2b7f0dd3-d728-4c3b-a825-926f668ca230 · inbound

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis cites this paper.

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:24:21.568593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 218c7587-de84-4637-9a81-00f260e3a254 · inbound

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis cites this paper.

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T11:15:44.767231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96542447-41f0-4a57-8713-e11d81459df3 · inbound

Large Causal Models for Temporal Causal Discovery cites this paper.

Large Causal Models for Temporal Causal Discovery Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T06:03:32.284798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a25ec38-0d88-40e2-ba3d-2009aeaf3e16 · inbound

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning cites this paper.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:09.690612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f3468a77-6077-474d-b90c-47fa7e658ea7 · inbound

TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization cites this paper.

TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T17:01:07.982047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d0c8f095-9896-4958-82ee-f962459fb78e · inbound

TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning cites this paper.

TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.584562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9946d7b7-78b9-417e-b097-b1d181923dd0 · inbound

Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density cites this paper.

Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:43:22.438569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 661c1b5c-a7f7-471a-952f-dd7854dc07d2 · 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 Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:46:46.107949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6c588817-7941-4fb9-957d-7a77059fb351 · inbound

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models cites this paper.

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:46:56.980253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eb865cb4-4387-4a4a-9f8a-41cbd3f76d17 · inbound

Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting cites this paper.

Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:27:26.138376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 67bfb209-2160-468a-8865-89e57239767f · inbound

Does Normalization Choice Matter for Causal Large Time-Series Models? cites this paper.

Does Normalization Choice Matter for Causal Large Time-Series Models? Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:07:28.573516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 747f5123-0459-4a72-838f-2ba75b96be5c · inbound

PMDformer: Patch-Mean Decoupling Information Transformer for Long-term Forecasting cites this paper.

PMDformer: Patch-Mean Decoupling Information Transformer for Long-term Forecasting Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-04T13:19:50.479908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 49615edb-bf99-4e75-af81-aa4f6770a13b · inbound

Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics cites this paper.

Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:28:44.089312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.