Paper Citation Record · LEDGER

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers

As of 7 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2506.08641.

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

pith.paper-citation-record.v1
2506.08641 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

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measured 55 of 55 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-07T16:40:11.419566Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T23:36:28.788694Z

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation db6d39c5-9973-4c68-92d5-8d734b3529ff · outbound

This paper cites Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E

Reference 1

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Observation 48b0a395-4cb3-405a-9257-df062a0ced80 · outbound

This paper cites Reyes-Ortiz.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Reyes-Ortiz

Reference 2

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Observation 36bb6573-7ac9-45a2-b1b8-c450cd5b82af · outbound

This paper cites Maddix, Hao Wang, Michael W.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Maddix, Hao Wang, Michael W

Reference 3

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Observation 61f0d5dc-7e22-4582-8e1d-929458a3e19c · outbound

This paper cites The UEA multivariate time series classification archive, 2018.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers The UEA multivariate time series classification archive, 2018

Reference 4

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Observation 7e982323-a623-424b-9045-39f1a0264397 · outbound

This paper cites Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models

Reference 5

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Observation 0b9fa3c5-83d0-40f6-8520-8bcd3d50ebd7 · outbound

This paper cites Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:00.527672Z

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Observation 9760cb42-6149-4456-ab92-a3cf1555affc · outbound

This paper cites LLM4TS: Aligning pre-trained llms as data-efficient time-series forecasters.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers LLM4TS: Aligning pre-trained llms as data-efficient time-series forecasters

Reference 7

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Observation 6bfb6756-49ea-48b9-865d-83176739019e · outbound

This paper cites VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters

Reference 8

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Observation ec8010f1-0646-4c1e-8352-fa1dac08bdaa · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Reproducible scaling laws for contrastive language-image learning

Reference 9

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Observation 5959ef29-267b-4ff7-872c-fab46346ec86 · outbound

This paper cites Clifford, Chengyu Liu, Benjamin Moody, Li-wei H.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Clifford, Chengyu Liu, Benjamin Moody, Li-wei H

Reference 10

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Observation 55b97ef0-98e0-4356-9631-1ecc45199c69 · outbound

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

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers A decoder-only foundation model for time-series forecasting

Reference 11

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Observation eafd268f-ea30-454f-bd97-4c30ddf5cd20 · outbound

This paper cites The ucr time series archive.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers The ucr time series archive

Reference 12

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Observation 821505ca-71d8-48b9-8d8a-29776089550e · outbound

This paper cites ImageNet: A large-scale hierarchical image database.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers ImageNet: A large-scale hierarchical image database

Reference 13

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This paper cites Heller, and Chris Donahue.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Heller, and Chris Donahue

Reference 14

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Observation 2d142ff1-f9e3-41fb-9e8a-8f36f492ccb5 · outbound

This paper cites From Colors to Classes: Emergence of Concepts in Vision Transformers.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers From Colors to Classes: Emergence of Concepts in Vision Transformers

Reference 15

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Observation 0b017cb4-f34d-4d18-b8af-8d7c42d367a2 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers An image is worth 16x16 words: Transformers for image recognition at scale

Reference 16

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Observation dd6a273e-2e3b-46e5-a5ea-3527c8aa916f · outbound

This paper cites Estimating the intrinsic dimension of datasets by a minimal neighborhood information.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Estimating the intrinsic dimension of datasets by a minimal neighborhood information

Reference 17

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Observation dd559a46-9409-4037-a000-8e0c7d5ab801 · outbound

This paper cites Mantis: Lightweight Foundation Model for Time Series Classification.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Mantis: Lightweight Foundation Model for Time Series Classification

Reference 18

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This paper cites UniTS: A unified multi-task time series model.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers UniTS: A unified multi-task time series model

Reference 19

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This paper cites DADApy: Distance-based analysis of data-manifolds in python.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers DADApy: Distance-based analysis of data-manifolds in python

Reference 20

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This paper cites Goldberger, Luis A.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Goldberger, Luis A

Reference 21

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This paper cites MOMENT: A family of open time-series foundation models.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers MOMENT: A family of open time-series foundation models

Reference 22

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Observation ceea55a6-b92f-45c6-b3b1-c7ec42234df6 · outbound

This paper cites Large language models are zero shot time series forecasters.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Large language models are zero shot time series forecasters

Reference 23

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Observation aa8f1598-4700-4c56-a401-2dc1ffffa4cf · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Momentum contrast for unsupervised visual representation learning

Reference 24

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This paper cites Masked autoencoders are scalable vision learners.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Masked autoencoders are scalable vision learners

Reference 25

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Observation 514ae33f-0835-48cf-a6bc-c32deb0582d7 · outbound

This paper cites Position: The platonic representation hypothesis.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Position: The platonic representation hypothesis

Reference 26

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This paper cites OpenCLIP, 2021.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers OpenCLIP, 2021

Reference 27

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Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Time-LLM: Time series forecasting by reprogram- ming large language models

Reference 28

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Observation b4b00f91-360e-4395-aa02-013e60cb9512 · outbound

This paper cites Zwinderman, Bert Tuk, Hilbert A.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Zwinderman, Bert Tuk, Hilbert A

Reference 29

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Observation eb1a603e-0005-4436-96d2-36802f534d8d · outbound

This paper cites Kimotho, Detmar Zimmer, and Walter Sextro.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Kimotho, Detmar Zimmer, and Walter Sextro

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:00.244953Z

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Observation 836cb598-150e-475e-8e07-aa1326199cf2 · outbound

This paper cites A theoretical understanding of shallow vision transformers: Learning, generalization, and sample complexity.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers A theoretical understanding of shallow vision transformers: Learning, generalization, and sample complexity

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:00.228218Z

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Observation afab980d-ff2d-4b3b-9e97-6539216ca5b5 · outbound

This paper cites Time series as images: Vision transformer for irregularly sampled time series.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Time series as images: Vision transformer for irregularly sampled time series

Reference 32

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Observation 81f2f3bb-d183-4639-8cbb-5a7a91c1324c · outbound

This paper cites NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining

Reference 33

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This paper cites SparseTSF: Modeling long-term time series forecasting with 1k parameters.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers SparseTSF: Modeling long-term time series forecasting with 1k parameters

Reference 34

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Observation d62cc8b4-d339-4fac-b980-0146ce3126d3 · outbound

This paper cites uWave: Accelerometer-based personalized gesture recognition and its applications.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers uWave: Accelerometer-based personalized gesture recognition and its applications

Reference 35

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Observation 69c64ddf-9448-4fe3-9d46-800e51e4ac11 · outbound

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

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 36

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Observation 529c6523-d575-4289-8ca4-df9e507c007a · outbound

This paper cites Timer: Generative pre-trained transformers are large time series models.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Timer: Generative pre-trained transformers are large time series models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:00.165045Z

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Observation 67a33fdb-2f73-47c0-8c96-fdce633c81d0 · outbound

This paper cites Harnessing Vision Models for Time Series Analysis: A Survey.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Harnessing Vision Models for Time Series Analysis: A Survey

Reference 38

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This paper cites A time series is worth 64 words: Long-term forecasting with transformers.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers A time series is worth 64 words: Long-term forecasting with transformers

Reference 39

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Observation 155bcd3e-a806-43a2-a9c6-b72e62f92b35 · outbound

This paper cites Generalized feature extraction for structural pattern recognition in time-series data.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Generalized feature extraction for structural pattern recognition in time-series data

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:00.140087Z

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Observation 59f265be-4c6f-471a-8446-2e27a703316e · outbound

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Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Unresolved cited work

Reference 41

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This paper cites Learning transferable visual models from natural language supervision.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Learning transferable visual models from natural language supervision

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:00.109628Z

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Observation 75f5da71-2c85-4d3d-860f-9501f7814b86 · outbound

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

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Reference 43

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Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers LAION-5B: An open large-scale dataset for training next generation image-text models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:00.094649Z

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Observation feafa55b-6f6b-466e-aaaf-acb76d077ef6 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 45

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This paper cites The geometry of hidden representations of large transformer models.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers The geometry of hidden representations of large transformer models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:00.078280Z

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Observation 650bfa24-0373-46bf-9930-ffceea498ba6 · outbound

This paper cites Attention is all you need.Advances in Neural Information Processing Systems, pages 5998–6008, 2017.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Attention is all you need.Advances in Neural Information Processing Systems, pages 5998–6008, 2017

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:00.061108Z

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Observation 9e49d0a6-4bd2-4cb3-8fb3-2656860ff25b · outbound

This paper cites Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:15:59.792632Z

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Observation 042dcdb1-6b4e-4ef4-9938-9a1d0f5de921 · outbound

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Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers TimesNet: Tem- poral 2d-variation modeling for general time series analysis

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:00.043374Z

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Observation dd7e0de9-4ea7-44b5-ac37-319c78b1cf5a · outbound

This paper cites SonicVisionLM: Playing sound with vision language models.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers SonicVisionLM: Playing sound with vision language models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:00.021985Z

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Observation 1c66808e-8e22-49ef-98ff-98a584cc4230 · outbound

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Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:16:00.004148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c07b87aa-2081-4258-a818-4f164efa526c · outbound

This paper cites Sigmoid loss for language image pre-training.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Sigmoid loss for language image pre-training

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:15:59.988993Z

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Observation cf6633e5-60e5-49f5-88cc-50f44d235a5f · outbound

This paper cites Davison, Hui Ren, Jing Huang, Chen Chen, Yuyin Zhou, Sunyang Fu, Wei Liu, Tianming Liu, Xiang Li, Yong Chen, Lifang He, James Zou, Quanzheng Li, Hongfang Liu, and Lichao Sun.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Davison, Hui Ren, Jing Huang, Chen Chen, Yuyin Zhou, Sunyang Fu, Wei Liu, Tianming Liu, Xiang Li, Yong Chen, Lifang He, James Zou, Quanzheng Li, Hongfang Liu, and Lichao Sun

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:15:59.972561Z

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Observation 0a79d2fc-3aaf-48a8-a328-80176250c6c2 · outbound

This paper cites One fits all: Power general time series analysis by pretrained lm.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers One fits all: Power general time series analysis by pretrained lm

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:15:59.958169Z

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Pith citing papers

Observation 59b3742e-42ed-4793-8232-120371e2a554 · inbound

EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming cites this paper.

EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:36:28.849467Z

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