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Paper Citation Record · LEDGER

CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2202.01575.

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

pith.paper-citation-record.v1
2202.01575 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:52:23.162319Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:38:43.318688Z

Reference resolution

0 of 0 outbound references displayed

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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 78532c09-7e4c-4d3b-8abe-8af5cb8d8f8e · inbound

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting cites this paper.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 21

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Source-reported events for the cited work

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Observation ef61b33f-0466-4d1b-b07b-0fda657dfe2a · inbound

Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming Data cites this paper.

Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming Data CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 13

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no resolver link, observed 2026-08-12T10:56:09.160404Z

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Unavailable: canonical work link unavailable.

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Observation da14a745-ffb2-4a7f-a5e4-d3c8eab49c65 · inbound

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts cites this paper.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 45

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Unavailable: canonical work link unavailable.

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Observation eabef448-1129-46a1-a022-9d586634d28b · inbound

Enhancing Masked Time-Series Modeling via Dropping Patches cites this paper.

Enhancing Masked Time-Series Modeling via Dropping Patches CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 38

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 86d2d3d3-f418-4d91-824a-3501052db488 · inbound

Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings cites this paper.

Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 104

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ecf5d266-8cb4-4e44-8444-a94ea3a6a32f · inbound

D-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams cites this paper.

D-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 51

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Source-reported events for the cited work

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Observation 5b541a11-aaae-4602-9bb5-2fe23cca3b45 · inbound

BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics cites this paper.

BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 50

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no resolver link, observed 2026-08-07T14:02:00.727325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation cf20c798-5660-42f8-824b-005dbb806534 · inbound

Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory Queue cites this paper.

Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory Queue CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 36

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Unavailable: canonical work link unavailable.

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Observation e912d2b7-b28c-46fe-b7e2-888c696372b0 · inbound

eMargin: Revisiting Contrastive Learning with Margin-Based Separation cites this paper.

eMargin: Revisiting Contrastive Learning with Margin-Based Separation CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0eed49f3-519a-4e5f-b7a8-c598574caa65 · inbound

Concurrence: A dependence criterion for time series, applied to biological data cites this paper.

Concurrence: A dependence criterion for time series, applied to biological data CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 10

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arxiv_id, observed 2026-05-16T21:08:32.592670Z

Source-reported events for the cited work

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Observation 78ab8b09-f037-4d2f-8d32-db066ab75c77 · inbound

TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale cites this paper.

TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 48

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arxiv_id, observed 2026-05-11T10:36:04.209732Z

Source-reported events for the cited work

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Observation fcf12439-6aa0-4a62-9e1c-3e9ff001e1c7 · inbound

Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series cites this paper.

Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 14

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verified exact
arxiv_id, observed 2026-05-22T08:26:16.627441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 886fc81f-d826-4f04-a1a4-d35eda611a97 · inbound

Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting cites this paper.

Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 34

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arxiv_id, observed 2026-07-03T04:07:37.207428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation de04ffa3-167d-4712-8a61-fbbaf2380964 · inbound

Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations cites this paper.

Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 20

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verified exact
arxiv_id, observed 2026-07-02T15:37:05.837829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 0f410e9e-2878-42ed-8f13-acead73e6e6c · inbound

LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning cites this paper.

LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 25

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verified exact
arxiv_id, observed 2026-07-02T15:37:06.022060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 6a90987d-c4ef-4452-a18a-a615d1468d57 · inbound

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

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 68

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arxiv_id, observed 2026-07-03T17:38:43.321274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 89dc91cc-64c9-420b-bde6-ff59c5637bb0 · inbound

CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation cites this paper.

CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 43

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