Paper Citation Record · LEDGER

FITS: Modeling Time Series with $10k$ Parameters

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2307.03756.

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

pith.paper-citation-record.v1
2307.03756 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:27:56.122047Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T21:17:35.815777Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 fbbe2ab5-81a1-47bd-be8d-8d93f57d84e8 · inbound

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

Deep Time Series Models: A Comprehensive Survey and Benchmark FITS: Modeling Time Series with $10k$ Parameters

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:05:51.473947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e896ad3b-9125-4eee-a08d-a495b2a35cd6 · inbound

FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting cites this paper.

FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting FITS: Modeling Time Series with $10k$ Parameters

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T15:58:14.767278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eca0a3b7-739c-421d-9ffb-ff8c4797b632 · inbound

BEAT: Balanced Frequency Adaptive Tuning for Long-Term Time-Series Forecasting cites this paper.

BEAT: Balanced Frequency Adaptive Tuning for Long-Term Time-Series Forecasting FITS: Modeling Time Series with $10k$ Parameters

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T21:31:36.048343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 853d18d6-12b1-4fa0-9895-c3aefbb7ef3b · inbound

Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts cites this paper.

Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts FITS: Modeling Time Series with $10k$ Parameters

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T04:27:56.122047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20454fab-5548-4295-859d-27ce6b24866e · inbound

A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series Forecasting cites this paper.

A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series Forecasting FITS: Modeling Time Series with $10k$ Parameters

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:04:14.888808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a088207-c675-4706-a7d2-26ed79d19756 · inbound

LightGTS: A Lightweight General Time Series Forecasting Model cites this paper.

LightGTS: A Lightweight General Time Series Forecasting Model FITS: Modeling Time Series with $10k$ Parameters

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T06:08:57.039509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccc7c596-ffe5-4b40-997f-b33c32257d2b · inbound

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series cites this paper.

When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series FITS: Modeling Time Series with $10k$ Parameters

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T21:43:07.654322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c402fbaa-54bc-4ce1-bfed-a68acc111805 · inbound

MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models cites this paper.

MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models FITS: Modeling Time Series with $10k$ Parameters

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T19:08:15.926564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9974106-33fe-41ad-8da8-2e4ae8678d3d · inbound

Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting cites this paper.

Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting FITS: Modeling Time Series with $10k$ Parameters

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T18:01:27.850889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33721db2-e4f9-44a0-beb1-b55ee71b3fdc · inbound

Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services cites this paper.

Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services FITS: Modeling Time Series with $10k$ Parameters

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T16:41:28.057972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0f39e11-0c31-4847-9882-9d32b0072bc6 · inbound

Time Series Forecasting Through the Lens of Dynamics cites this paper.

Time Series Forecasting Through the Lens of Dynamics FITS: Modeling Time Series with $10k$ Parameters

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-19T03:32:01.321062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d1c3d9aa-331d-44d0-8a6e-5c3481621f9d · 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 FITS: Modeling Time Series with $10k$ Parameters

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:25:34.143603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5a0d3338-c2ed-42de-80ce-3066a126090f · inbound

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting cites this paper.

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting FITS: Modeling Time Series with $10k$ Parameters

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:51:23.413175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 68eb6b57-eb24-4257-b77a-becb6c3a4df4 · inbound

Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting cites this paper.

Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting FITS: Modeling Time Series with $10k$ Parameters

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T02:44:18.163604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3843aa8d-458a-4719-884d-030ee267132d · inbound

Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection cites this paper.

Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection FITS: Modeling Time Series with $10k$ Parameters

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-14T22:02:51.907844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffe2966a-a97a-4373-8b55-bf05835ffe16 · inbound

SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies cites this paper.

SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies FITS: Modeling Time Series with $10k$ Parameters

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:58:29.219782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8ea584cc-55ad-4aed-82da-37bb07964676 · inbound

GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery cites this paper.

GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery FITS: Modeling Time Series with $10k$ Parameters

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:44:42.819747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6a88e58a-963d-459f-97c9-ad4ac58dc3ff · inbound

Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins cites this paper.

Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins FITS: Modeling Time Series with $10k$ Parameters

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:37:09.012106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a4f7b7cf-b1ca-421d-858a-c48128ee1312 · inbound

Enhancing deep learning models for time series classification via knowledge distillation cites this paper.

Enhancing deep learning models for time series classification via knowledge distillation FITS: Modeling Time Series with $10k$ Parameters

Reference 46

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T21:17:35.829738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.