Paper Citation Record · LEDGER

TimeGPT-1

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 38 inbound Pith citation observations for arXiv:2310.03589.

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

pith.paper-citation-record.v1
2310.03589 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 38 of 38 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:23:09.322846Z

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 741e0c07-cab5-4929-8f69-ca69c3476438 · inbound

A decoder-only foundation model for time-series forecasting cites this paper.

A decoder-only foundation model for time-series forecasting TimeGPT-1

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:07:21.305025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e42ed69a-9d9d-4a6f-9d83-57b3a76c07bc · inbound

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

Deep Time Series Models: A Comprehensive Survey and Benchmark TimeGPT-1

Reference 199

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:05:51.326646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 10f81e10-196d-4afb-9648-ad53da79c612 · inbound

TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis cites this paper.

TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis TimeGPT-1

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:45:47.230675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a5cf3bf9-2181-4e62-97d3-da682543fbf1 · inbound

Tube Loss: A Novel Approach for Prediction Interval Estimation cites this paper.

Tube Loss: A Novel Approach for Prediction Interval Estimation TimeGPT-1

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-23T07:47:42.593880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 60ce9eeb-2195-453a-87bd-20fcd5e57ac4 · inbound

Out-of-Distribution Generalization in Time Series: A Survey cites this paper.

Out-of-Distribution Generalization in Time Series: A Survey TimeGPT-1

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-23T00:22:18.377467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 14ac2c0e-190a-4f0f-a57d-df64a42472f3 · 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 TimeGPT-1

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T13:23:09.322846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 381b5e9a-4509-45b5-9b42-9ce1fd9133b4 · inbound

TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models cites this paper.

TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models TimeGPT-1

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T10:37:06.818289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6609867-602c-43d1-b628-f2c35425d329 · inbound

Deep Learning Network-Temporal Models For Traffic Prediction cites this paper.

Deep Learning Network-Temporal Models For Traffic Prediction TimeGPT-1

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-14T22:51:55.339022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f90ee967-245b-43ca-aa57-0239fa09bade · inbound

Frequency-Guided Deformable Networks for Continuous Phase Alignment cites this paper.

Frequency-Guided Deformable Networks for Continuous Phase Alignment TimeGPT-1

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-13T20:39:48.872028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7700dfef-6ea5-4add-a37f-737204f914ca · 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 TimeGPT-1

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:53:08.568299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7462044f-0ae4-487c-ade1-04eb406fa81d · inbound

Discrete Prototypical Memories for Federated Time Series Foundation Models cites this paper.

Discrete Prototypical Memories for Federated Time Series Foundation Models TimeGPT-1

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:35:51.998168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 91f65edb-84a9-499c-92ad-0b684e4fff8c · inbound

Wearable AI in the Era of Large Sensor Models cites this paper.

Wearable AI in the Era of Large Sensor Models TimeGPT-1

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:30:58.093028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9eea6a47-becf-456a-b26e-3b7198d584dd · inbound

Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework cites this paper.

Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework TimeGPT-1

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:26:25.918576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ec53823e-9cc7-420e-901f-d581ce86de49 · inbound

TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models cites this paper.

TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models TimeGPT-1

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T12:04:38.796054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 514f374d-55c9-4e1c-a02e-329ce2e22061 · inbound

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP cites this paper.

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP TimeGPT-1

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T16:05:48.856087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1318fb0a-62b9-4f28-a21c-c7b7362010e3 · inbound

FHRFormer: A Self-Supervised Masked Transformer Framework for Fetal Heart Rate Time-Series Inpainting and Forecasting cites this paper.

FHRFormer: A Self-Supervised Masked Transformer Framework for Fetal Heart Rate Time-Series Inpainting and Forecasting TimeGPT-1

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-06-29T07:13:17.156311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0ed8ba2a-37e8-484f-ad8f-02bb87dcb2c7 · inbound

Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling cites this paper.

Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling TimeGPT-1

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T19:33:54.166610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9a55c10e-d5bf-4b3f-a65a-ed10436a9212 · inbound

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection cites this paper.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection TimeGPT-1

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:16:14.104647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 723fa841-65b8-46a4-88cb-ba785e4f724e · inbound

Towards Intrusion Detection Systems for RPL-based IoT Networks using Foundation Models cites this paper.

Towards Intrusion Detection Systems for RPL-based IoT Networks using Foundation Models TimeGPT-1

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:56:35.006416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e9507ac3-ba9b-4c00-ad28-bfcdf9813204 · inbound

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series cites this paper.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series TimeGPT-1

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:47:23.114904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ca34f364-baca-4e8f-ac1d-c30ad2b21f27 · inbound

UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation cites this paper.

UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation TimeGPT-1

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-03T04:37:36.606614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0155af0b-ab5a-4cdf-9e7e-8dad719abefa · inbound

WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning cites this paper.

WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning TimeGPT-1

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T21:08:58.454968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e5e117e9-73b2-4582-b02d-8ede89970bba · inbound

MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios cites this paper.

MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios TimeGPT-1

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T16:09:56.452773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3cf9862e-d4b9-42f9-9e0d-c52ff4abe3b1 · inbound

Pretrained Time-Series Foundation Models for Financial Return Forecasting cites this paper.

Pretrained Time-Series Foundation Models for Financial Return Forecasting TimeGPT-1

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T15:29:56.385723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2482b7c2-e233-46f6-8b1d-f1a953501597 · inbound

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings cites this paper.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings TimeGPT-1

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:13:52.912021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a3dd5446-313e-44e5-aa67-6821f2b3f617 · inbound

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis cites this paper.

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis TimeGPT-1

Reference 100

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T17:38:43.311229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 84f37046-3b1a-4769-bf1e-415416f55bcc · 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 TimeGPT-1

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:28:44.086406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8301df50-8136-4f8c-b79c-84a327973aff · inbound

Modular Foundation Models for Time-Series Perception in Digital Twins cites this paper.

Modular Foundation Models for Time-Series Perception in Digital Twins TimeGPT-1

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-12T01:22:51.284207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49cb55e1-4e19-437a-98e8-c5e6419e73ca · 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 TimeGPT-1

Reference 74

Resolution: metadata mismatch; local_arxiv, observed 2026-07-07T19:34:06.415438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 93e2fa8e-238c-4dbb-923d-91d11d9dd8ac · inbound

From Vector Autoregressions to AI-based Time Series Forecasting: A Review cites this paper.

From Vector Autoregressions to AI-based Time Series Forecasting: A Review TimeGPT-1

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T02:39:07.879221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a7e65b5-bdb6-4200-9945-122048767bb8 · inbound

A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods cites this paper.

A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods TimeGPT-1

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T22:35:47.227092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f895679-3aaf-4219-ab19-9378f0abf902 · inbound

Residual-Guided Multi-Resolution Refinement of Foundation Models: A Case Study in Drought Forecasting cites this paper.

Residual-Guided Multi-Resolution Refinement of Foundation Models: A Case Study in Drought Forecasting TimeGPT-1

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T17:50:48.858348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58ac0963-5f92-447f-b3b0-42f96e378818 · inbound

Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting cites this paper.

Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting TimeGPT-1

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T17:48:58.864822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f568d2c5-df2b-4935-8242-472750f61fce · inbound

Trend strength predicts when generative foundation models win: a power-controlled benchmark, a mechanism, and an actionable selection rule cites this paper.

Trend strength predicts when generative foundation models win: a power-controlled benchmark, a mechanism, and an actionable selection rule TimeGPT-1

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T09:17:11.933398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5921e5e0-ae50-4c9e-b492-c4a4d296430d · inbound

Post-Training in Time Series Foundation Models: A Unifying Framework cites this paper.

Post-Training in Time Series Foundation Models: A Unifying Framework TimeGPT-1

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T11:08:05.342371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e84f46a4-17e6-4305-aa2a-22924fe17ac6 · inbound

DRP-FLR: Data-Driven Assessment of Demand Response Potential for Flexible Load Regulation in Smart Grids cites this paper.

DRP-FLR: Data-Driven Assessment of Demand Response Potential for Flexible Load Regulation in Smart Grids TimeGPT-1

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T11:51:04.712673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f89d808e-0416-478d-b99e-6daa157bf9cd · inbound

Contextual Deconvolution for Variance-Stable Demand Sensing: Kernel-Modulated Operators in Promotional Retail cites this paper.

Contextual Deconvolution for Variance-Stable Demand Sensing: Kernel-Modulated Operators in Promotional Retail TimeGPT-1

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-01T01:54:54.224950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39a0fd8d-d4f0-4266-89e8-1fa2e6507838 · inbound

RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment cites this paper.

RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment TimeGPT-1

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T12:00:32.380856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.