Paper Citation Record · LEDGER

Harnessing Vision Models for Time Series Analysis: A Survey

As of 10 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 8 inbound Pith citation observations for arXiv:2502.08869.

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

pith.paper-citation-record.v1
2502.08869 v2

Coverage vector

measured 72 of 72 reference resolution

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measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:00:07.915748Z

Reference resolution

72 of 72 outbound references displayed

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External citation measurements

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Outbound references

Observation a41ed466-acdf-4573-ba9d-4bb61e0ad166 · outbound

This paper cites GPT-4 Technical Report.

Harnessing Vision Models for Time Series Analysis: A Survey GPT-4 Technical Report

Reference 1

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Observation 31a86185-58de-47bf-af5e-4ca5532dc472 · outbound

This paper cites MAE-AST: Masked Autoencoding Audio Spectrogram Transformer.

Harnessing Vision Models for Time Series Analysis: A Survey MAE-AST: Masked Autoencoding Audio Spectrogram Transformer

Reference 4

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Observation c39804b8-2e79-4d7b-a323-30d777d03831 · outbound

This paper cites Deep learning and time series-to-image encoding for financial forecasting.

Harnessing Vision Models for Time Series Analysis: A Survey Deep learning and time series-to-image encoding for financial forecasting

Reference 7

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This paper cites Trading via image classification.

Harnessing Vision Models for Time Series Analysis: A Survey Trading via image classification

Reference 9

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Observation d9c6b159-ab00-4755-b014-100fae3093ee · outbound

This paper cites Fusion of image representations for time series classification with deep learning.

Harnessing Vision Models for Time Series Analysis: A Survey Fusion of image representations for time series classification with deep learning

Reference 10

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Observation 3d2d8dc4-bf08-48ab-8a61-b76c252968d9 · outbound

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

Harnessing Vision Models for Time Series Analysis: A Survey A decoder-only foundation model for time-series forecasting

Reference 11

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Observation 3ccc7428-0077-471f-8d9c-132bd4b6316c · outbound

This paper cites Plots Unlock Time-Series Understanding in Multimodal Models.

Harnessing Vision Models for Time Series Analysis: A Survey Plots Unlock Time-Series Understanding in Multimodal Models

Reference 12

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Observation 91024a87-6813-4e1c-8b67-a4cb7f384aa3 · outbound

This paper cites The wavelet transform, time-frequency localization and signal analysis.

Harnessing Vision Models for Time Series Analysis: A Survey The wavelet transform, time-frequency localization and signal analysis

Reference 13

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Observation 42e15461-4460-4f5d-b35d-6bcdc22a8f63 · outbound

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

Harnessing Vision Models for Time Series Analysis: A Survey An image is worth 16x16 words: Transformers for image recognition at scale

Reference 15

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Observation ad80981a-671b-45f3-a6ad-7d64f472f3a2 · outbound

This paper cites Recurrence plots of dynamical systems.

Harnessing Vision Models for Time Series Analysis: A Survey Recurrence plots of dynamical systems

Reference 17

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Observation f34df075-80df-4a66-9df1-f674166e3514 · outbound

This paper cites Ssast: Self-supervised audio spectrogram trans- former.

Harnessing Vision Models for Time Series Analysis: A Survey Ssast: Self-supervised audio spectrogram trans- former

Reference 19

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Observation 71f666ad-de15-48e6-942a-00356ac4239e · outbound

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

Harnessing Vision Models for Time Series Analysis: A Survey Moment: A family of open time-series foundation models

Reference 20

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This paper cites Signal estima- tion from modified short-time fourier transform.

Harnessing Vision Models for Time Series Analysis: A Survey Signal estima- tion from modified short-time fourier transform

Reference 21

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Observation 878aeed5-f483-4bad-8a70-a427f9a97b14 · outbound

This paper cites Deep residual learning for image recognition.

Harnessing Vision Models for Time Series Analysis: A Survey Deep residual learning for image recognition

Reference 23

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Observation 13342413-16d1-45eb-950e-ea78a408f75b · outbound

This paper cites Empowering time series analy- sis with large language models: A survey.

Harnessing Vision Models for Time Series Analysis: A Survey Empowering time series analy- sis with large language models: A survey

Reference 25

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Observation 3c96db80-ae33-4e24-a42e-69c2416967c3 · outbound

This paper cites Time-llm: Time series forecasting by re- programming large language models.

Harnessing Vision Models for Time Series Analysis: A Survey Time-llm: Time series forecasting by re- programming large language models

Reference 26

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Observation e288dac6-6026-41a3-a625-f532ddb04198 · outbound

This paper cites Multi-sensor data fusion and time series to image encoding for hardness recognition.

Harnessing Vision Models for Time Series Analysis: A Survey Multi-sensor data fusion and time series to image encoding for hardness recognition

Reference 27

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Observation 9499b39c-92a3-4499-95f0-27629621e677 · outbound

This paper cites TimEHR: Image-based Time Series Generation for Electronic Health Records.

Harnessing Vision Models for Time Series Analysis: A Survey TimEHR: Image-based Time Series Generation for Electronic Health Records

Reference 28

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Observation 76e1f9f7-4c98-4760-abd6-c24519886f6d · outbound

This paper cites Reversible instance normalization for accurate time-series forecasting against distribution shift.

Harnessing Vision Models for Time Series Analysis: A Survey Reversible instance normalization for accurate time-series forecasting against distribution shift

Reference 29

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Observation 03b5bc26-7e2c-4041-916f-72c8432feeb9 · outbound

This paper cites Cafo: Feature-centric explanation on time series classification.

Harnessing Vision Models for Time Series Analysis: A Survey Cafo: Feature-centric explanation on time series classification

Reference 30

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Observation 39577b1a-e20e-4c39-8e09-81ac72d6c174 · outbound

This paper cites Convolutional neural networks for energy time se- ries forecasting.

Harnessing Vision Models for Time Series Analysis: A Survey Convolutional neural networks for energy time se- ries forecasting

Reference 31

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Observation 1dac95a5-dad3-411d-96ae-f2bd39a1ec5b · outbound

This paper cites Time-series bitmaps: a practical visualization tool for working with large time series databases.

Harnessing Vision Models for Time Series Analysis: A Survey Time-series bitmaps: a practical visualization tool for working with large time series databases

Reference 32

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Observation 03d70152-3302-4365-9901-15639822a21d · outbound

This paper cites Tts-gan: A transformer-based time- series generative adversarial network.

Harnessing Vision Models for Time Series Analysis: A Survey Tts-gan: A transformer-based time- series generative adversarial network

Reference 34

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Observation 0b8e3c71-ee0a-4a3e-a201-b2b4f206efa1 · outbound

This paper cites Foundation models for time series analysis: A tutorial and survey.

Harnessing Vision Models for Time Series Analysis: A Survey Foundation models for time series analysis: A tutorial and survey

Reference 35

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Observation 5e0dbb72-9f65-48e7-80de-7f37046c1311 · outbound

This paper cites Hierarchical context representation and self-adaptive thresholding for multivariate anomaly detection.

Harnessing Vision Models for Time Series Analysis: A Survey Hierarchical context representation and self-adaptive thresholding for multivariate anomaly detection

Reference 36

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Observation 58e9caf8-03cd-4300-af45-a71c1167c561 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Harnessing Vision Models for Time Series Analysis: A Survey Swin transformer: Hierarchical vision transformer using shifted windows

Reference 37

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This paper cites Visual instruction tuning.

Harnessing Vision Models for Time Series Analysis: A Survey Visual instruction tuning

Reference 38

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Observation 4cc37a69-14e8-490d-91b6-93c628b6dcda · outbound

This paper cites Learning traffic as images: A deep convolutional neural network for large-scale transporta- tion network speed prediction.

Harnessing Vision Models for Time Series Analysis: A Survey Learning traffic as images: A deep convolutional neural network for large-scale transporta- tion network speed prediction

Reference 39

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Observation 541e7c2a-0dfc-4f2c-8f36-931ba686cc65 · outbound

This paper cites Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models.

Harnessing Vision Models for Time Series Analysis: A Survey Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models

Reference 41

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Observation cad7eb0f-1d9b-4f3f-9143-eeb2b9ce5186 · outbound

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

Harnessing Vision Models for Time Series Analysis: A Survey A time series is worth 64 words: Long- term forecasting with transformers

Reference 42

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Observation 2a4c8269-efda-494c-919e-7ab4deab09d5 · outbound

This paper cites S 2ip-llm: Semantic space informed prompt learning with llm for time series forecasting.

Harnessing Vision Models for Time Series Analysis: A Survey S 2ip-llm: Semantic space informed prompt learning with llm for time series forecasting

Reference 43

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Observation 736fbb35-b5f2-4d88-a36b-bc4629bde43d · outbound

This paper cites On the Feasibility of Vision-Language Models for Time-Series Classification.

Harnessing Vision Models for Time Series Analysis: A Survey On the Feasibility of Vision-Language Models for Time-Series Classification

Reference 44

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This paper cites Image- based time series forecasting: A deep convolutional neural network approach.

Harnessing Vision Models for Time Series Analysis: A Survey Image- based time series forecasting: A deep convolutional neural network approach

Reference 45

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Observation 744f831a-1457-4ead-96fa-eecee25f9380 · outbound

This paper cites Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.

Harnessing Vision Models for Time Series Analysis: A Survey Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 46

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This paper cites Time series classification using compression distance of recurrence plots.

Harnessing Vision Models for Time Series Analysis: A Survey Time series classification using compression distance of recurrence plots

Reference 47

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This paper cites Going deeper with convolutions.

Harnessing Vision Models for Time Series Analysis: A Survey Going deeper with convolutions

Reference 50

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Observation 426a88bb-99ed-4a1e-9a33-54fd4bb3d4a5 · outbound

This paper cites Are language models ac- tually useful for time series forecasting? In NeurIPS,.

Harnessing Vision Models for Time Series Analysis: A Survey Are language models ac- tually useful for time series forecasting? In NeurIPS,

Reference 51

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This paper cites Vits for sits: Vision transformers for satellite image time series.

Harnessing Vision Models for Time Series Analysis: A Survey Vits for sits: Vision transformers for satellite image time series

Reference 52

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This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Harnessing Vision Models for Time Series Analysis: A Survey Gemini: A Family of Highly Capable Multimodal Models

Reference 53

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Harnessing Vision Models for Time Series Analysis: A Survey Training data-efficient image transformers & distillation through attention

Reference 54

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Harnessing Vision Models for Time Series Analysis: A Survey WaveNet: A Generative Model for Raw Audio

Reference 55

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Harnessing Vision Models for Time Series Analysis: A Survey Wavelets and filter banks: Theory and design.IEEE Trans

Reference 56

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This paper cites Timemixer++: A general time series pattern machine for universal predictive analysis.

Harnessing Vision Models for Time Series Analysis: A Survey Timemixer++: A general time series pattern machine for universal predictive analysis

Reference 57

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Harnessing Vision Models for Time Series Analysis: A Survey Leveraging Vision-Language Models for Granular Market Change Prediction

Reference 59

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This paper cites Timesnet: Temporal 2d-variation model- ing for general time series analysis.

Harnessing Vision Models for Time Series Analysis: A Survey Timesnet: Temporal 2d-variation model- ing for general time series analysis

Reference 60

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Observation 547a527c-a1f7-46b0-9b23-f8554b909ff2 · outbound

This paper cites Large Multimodal Agents: A Survey.

Harnessing Vision Models for Time Series Analysis: A Survey Large Multimodal Agents: A Survey

Reference 61

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This paper cites Your time series is worth a binary image: machine vision assisted deep framework for time series forecasting.

Harnessing Vision Models for Time Series Analysis: A Survey Your time series is worth a binary image: machine vision assisted deep framework for time series forecasting

Reference 62

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This paper cites Vitime: A visual intelligence-based foundation model for time series forecasting.

Harnessing Vision Models for Time Series Analysis: A Survey Vitime: A visual intelligence-based foundation model for time series forecasting

Reference 63

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This paper cites Multivariate Time Series Classification using Dilated Convolutional Neural Network.

Harnessing Vision Models for Time Series Analysis: A Survey Multivariate Time Series Classification using Dilated Convolutional Neural Network

Reference 64

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Observation 1b37d71c-4231-48c8-8c59-8a0eed90c9be · outbound

This paper cites From pixels to predictions: Spectrogram and vision transformer for better time series forecasting.

Harnessing Vision Models for Time Series Analysis: A Survey From pixels to predictions: Spectrogram and vision transformer for better time series forecasting

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:27:13.989942Z

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Observation 0373ac26-5eeb-41c1-a3d7-fad342962e84 · outbound

This paper cites Deep spatio-temporal residual networks for citywide crowd flows pre- diction.

Harnessing Vision Models for Time Series Analysis: A Survey Deep spatio-temporal residual networks for citywide crowd flows pre- diction

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:27:13.972668Z

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Observation ee0a98b5-b448-424f-9f0d-b3ec3bc1f535 · outbound

This paper cites A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data.

Harnessing Vision Models for Time Series Analysis: A Survey A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:27:13.957773Z

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This paper cites Tapnet: Multivariate time series classification with attentional prototypical network.

Harnessing Vision Models for Time Series Analysis: A Survey Tapnet: Multivariate time series classification with attentional prototypical network

Reference 69

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This paper cites Insight miner: A time series analysis dataset for cross-domain alignment with natural language.

Harnessing Vision Models for Time Series Analysis: A Survey Insight miner: A time series analysis dataset for cross-domain alignment with natural language

Reference 70

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This paper cites Large language models for time series: A survey.

Harnessing Vision Models for Time Series Analysis: A Survey Large language models for time series: A survey

Reference 71

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This paper cites See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers.

Harnessing Vision Models for Time Series Analysis: A Survey See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers

Reference 72

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This paper cites Classification of time-series images using deep convo- lutional neural networks.

Harnessing Vision Models for Time Series Analysis: A Survey Classification of time-series images using deep convo- lutional neural networks

Reference 1984

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This paper cites Ast: Audio spectrogram transformer.

Harnessing Vision Models for Time Series Analysis: A Survey Ast: Audio spectrogram transformer

Reference 1987

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This paper cites Vision language models are few-shot audio spectrogram classi- fiers.

Harnessing Vision Models for Time Series Analysis: A Survey Vision language models are few-shot audio spectrogram classi- fiers

Reference 1990

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:27:14.595297Z

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Harnessing Vision Models for Time Series Analysis: A Survey Forecasting with time series imaging

Reference 2005

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This paper cites Very deep convolutional networks for large-scale im- age recognition.

Harnessing Vision Models for Time Series Analysis: A Survey Very deep convolutional networks for large-scale im- age recognition

Reference 2013

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This paper cites Visual time series forecasting: an image-driven approach.

Harnessing Vision Models for Time Series Analysis: A Survey Visual time series forecasting: an image-driven approach

Reference 2015

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This paper cites Masked autoencoders are scalable vision learners.

Harnessing Vision Models for Time Series Analysis: A Survey Masked autoencoders are scalable vision learners

Reference 2016

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Observation 86d1d4c5-f1da-4249-b0c3-1a7eb8a74b1b · outbound

This paper cites Utilizing image transforms and diffusion models for generative modeling of short and long time series.

Harnessing Vision Models for Time Series Analysis: A Survey Utilizing image transforms and diffusion models for generative modeling of short and long time series

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:27:14.236656Z

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This paper cites Beit: Bert pre-training of image transformers.

Harnessing Vision Models for Time Series Analysis: A Survey Beit: Bert pre-training of image transformers

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:27:14.685816Z

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Observation d65aef1a-337e-4e4d-92e8-e87f3d7b9d0f · outbound

This paper cites Deep video pre- diction for time series forecasting.

Harnessing Vision Models for Time Series Analysis: A Survey Deep video pre- diction for time series forecasting

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:27:14.006766Z

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Observation 53e4f349-bb19-4e50-b99f-5e1d20287fd5 · outbound

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

Harnessing Vision Models for Time Series Analysis: A Survey VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters

Reference 2020

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This paper cites Image Processing Tools for Financial Time Series Classification.

Harnessing Vision Models for Time Series Analysis: A Survey Image Processing Tools for Financial Time Series Classification

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-07T23:27:13.799085Z

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Observation 84ca1351-5a7f-430f-947c-c2ccf1ebd5ec · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Harnessing Vision Models for Time Series Analysis: A Survey An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 2022

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

Harnessing Vision Models for Time Series Analysis: A Survey Chronos: Learning the Language of Time Series

Reference 2023

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This paper cites The claude 3 model family: Opus, sonnet, haiku.

Harnessing Vision Models for Time Series Analysis: A Survey The claude 3 model family: Opus, sonnet, haiku

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:27:14.700844Z

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Observation 6623ed3f-8455-4c4c-9843-bf8d72346172 · outbound

This paper cites Transformers in time series: a survey.

Harnessing Vision Models for Time Series Analysis: A Survey Transformers in time series: a survey

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:27:14.038439Z

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Pith citing papers

Observation cf8268cc-0503-4688-a0a4-02f76cfff16c · inbound

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Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Framework Harnessing Vision Models for Time Series Analysis: A Survey

Reference 32

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Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers cites this paper.

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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Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning Harnessing Vision Models for Time Series Analysis: A Survey

Reference 34

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Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning cites this paper.

Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning Harnessing Vision Models for Time Series Analysis: A Survey

Reference 34

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VESTA: Visual Exploration with Statistical Tool Agents cites this paper.

VESTA: Visual Exploration with Statistical Tool Agents Harnessing Vision Models for Time Series Analysis: A Survey

Reference 34

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Representing Time Series as Structured Programs for LLM Reasoning cites this paper.

Representing Time Series as Structured Programs for LLM Reasoning Harnessing Vision Models for Time Series Analysis: A Survey

Reference 57

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Observation dfdc79c3-4b3e-418a-b0d2-2688552e5c57 · inbound

Naturalness Predicts but Does Not Cause Transferability in Image Encodings of Real-World Streams cites this paper.

Naturalness Predicts but Does Not Cause Transferability in Image Encodings of Real-World Streams Harnessing Vision Models for Time Series Analysis: A Survey

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:00:07.917868Z

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Observation 848b5129-ad77-4ee3-a1fe-91a5059b5c79 · inbound

VEIL: How Visual Encoding Hijacking Induces Bias In Vision Models cites this paper.

VEIL: How Visual Encoding Hijacking Induces Bias In Vision Models Harnessing Vision Models for Time Series Analysis: A Survey

Reference 26

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