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Paper Citation Record · LEDGER

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting

As of 23 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.22299.

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

pith.paper-citation-record.v1
2607.22299 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T05:16:39.547505Z

measured 23 of 23 standing notices

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

measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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Reference resolution

23 of 23 outbound references displayed

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External citation measurements

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Outbound references

Observation 58121357-6900-4cd2-97f5-96ccd1a33f4a · outbound

This paper cites GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation

Reference 1

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Observation 59b67d14-94d3-4e78-9a4e-8cb9d0a79f3a · outbound

This paper cites TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 3

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Observation 98f4ae6f-f64b-469a-8d5a-dc55daad3f74 · outbound

This paper cites Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series

Reference 7

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Observation 5c8a2717-fc73-44d6-96ec-47ffa778be2b · outbound

This paper cites UniTS: A Unified Multi-Task Time Series Model.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting UniTS: A Unified Multi-Task Time Series Model

Reference 8

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Observation 4f43b1e0-a18b-4d57-af5f-0458a938eff5 · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 11

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Observation 46cc2ba2-0b7c-4f52-af4d-5687a6dbdf8f · outbound

This paper cites N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting

Reference 16

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Observation f55f3edd-1638-4d78-9eaf-9850d687b13f · outbound

This paper cites Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Reference 17

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Observation 962be7cc-6ddd-4711-8b6d-17ec74fc3209 · outbound

This paper cites TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables

Reference 18

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Observation 15f7b1fb-e8c9-491e-b49e-4b83b004c8e9 · outbound

This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 19

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Observation 86f1ac76-55cb-4d29-b36a-4ac2dc63cb94 · outbound

This paper cites FlexTSF: A Flexible Forecasting Model for Time Series with Variable Regularities.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting FlexTSF: A Flexible Forecasting Model for Time Series with Variable Regularities

Reference 20

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Observation e9273034-9e94-44e4-bd51-f0c835d61a81 · outbound

This paper cites PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting

Reference 21

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Observation ab689037-4afe-4263-958f-8042c8fc9ca1 · outbound

This paper cites doi: 10.14778/3611540.3611561.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting doi: 10.14778/3611540.3611561

Reference 22

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Observation e67d8c6f-c928-4bce-9677-640400a38144 · outbound

This paper cites doi: 10.1162/neco.1991.3.1.79.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting doi: 10.1162/neco.1991.3.1.79

Reference 1991

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Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting Unresolved cited work

Reference 1994

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Observation aca4e3e5-cf8e-46e6-b566-7e135a676051 · outbound

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Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting Unresolved cited work

Reference 2006

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Observation 779272f2-3595-4e44-bfdd-2b152bed1018 · outbound

This paper cites doi: 10.1007/s10994-008-5051-0.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting doi: 10.1007/s10994-008-5051-0

Reference 2008

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Observation 571ad291-4fbc-4da9-b32e-b1c593b5fac1 · outbound

This paper cites URLhttps://doi.org/10.1214/14-EJS886.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting URLhttps://doi.org/10.1214/14-EJS886

Reference 2014

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Observation e5ab68a0-c413-4cf4-b9a1-dae7db081830 · outbound

This paper cites Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 2019

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Observation 2c3047ec-67fb-49e4-8469-861aec14efc9 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 2021

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Observation 1b329efa-64aa-41dc-982a-ff3b364a98e6 · outbound

This paper cites STD: A Seasonal-Trend-Dispersion Decomposition of Time Series.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting STD: A Seasonal-Trend-Dispersion Decomposition of Time Series

Reference 2022

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Observation 0bf4a477-c21f-4825-9016-ddd79cd602da · outbound

This paper cites Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting

Reference 2023

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Observation d9252b5f-dd58-418e-9e02-ee5d718837d9 · outbound

This paper cites Chronos: Learning the Language of Time Series.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting Chronos: Learning the Language of Time Series

Reference 2024

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Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting 23 Running Title for Header B

Reference 2025

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Pith citing papers

No inbound Pith citation observations are available.