Paper Citation Record · LEDGER

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2504.20099.

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

pith.paper-citation-record.v1
2504.20099 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

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measured 43 of 43 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 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: cited_works

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 3400c3ca-14bd-481b-bdf2-1eccc4358ae2 · outbound

This paper cites From requirement to solution: Unveiling problem-driven design patterns in visual analytics,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics From requirement to solution: Unveiling problem-driven design patterns in visual analytics,

Reference 1

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This paper cites Visual causal analysis of multivariate time series,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Visual causal analysis of multivariate time series,

Reference 2

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This paper cites Embedding projector - visualization of high-dimensional data,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Embedding projector - visualization of high-dimensional data,

Reference 3

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This paper cites Interactive visualizations,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Interactive visualizations,

Reference 4

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This paper cites Deepvats: Deep visual analytics for time series,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Deepvats: Deep visual analytics for time series,

Reference 5

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This paper cites Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain

Reference 6

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This paper cites Mixed contrastive transfer learning for few-shot workload predictioninthecloud,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Mixed contrastive transfer learning for few-shot workload predictioninthecloud,

Reference 7

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This paper cites A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data,

Reference 8

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This paper cites Transfer learning for time series classification,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Transfer learning for time series classification,

Reference 9

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Observation 3735c4c6-2115-4ec8-b4ed-e10767288466 · outbound

This paper cites MOMENT: A Family of Open Time-series Foundation Models.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics MOMENT: A Family of Open Time-series Foundation Models

Reference 10

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This paper cites Chronos: Learning the language of time series,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Chronos: Learning the language of time series,

Reference 11

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This paper cites Unified training of universal time series forecastingtransformers,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Unified training of universal time series forecastingtransformers,

Reference 12

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Observation 17e892fa-32b9-461a-b557-8e15b8ced56e · outbound

This paper cites On the integration of large-scale time series distance matrices into deep visual analytic tools,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics On the integration of large-scale time series distance matrices into deep visual analytic tools,

Reference 13

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This paper cites Exploring scalability in large-scale time seriesindeepvatsframework,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Exploring scalability in large-scale time seriesindeepvatsframework,

Reference 14

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Observation d2435474-1660-493a-b44a-0085c48d4114 · outbound

This paper cites Introducing mplots: scaling time series recurrence plots to massive datasets,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Introducing mplots: scaling time series recurrence plots to massive datasets,

Reference 15

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This paper cites A systematic review for transformer-based long-term series forecasting,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics A systematic review for transformer-based long-term series forecasting,

Reference 16

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This paper cites Are transformers effective for time series forecasting?,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Are transformers effective for time series forecasting?,

Reference 17

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This paper cites Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,

Reference 18

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Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Attention is all you need,

Reference 19

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Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Timexer: Empowering transformers for time series forecasting with exogenous variables,

Reference 20

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This paper cites Medformer: A multi-granularity patching transformer for medical time-series classification,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Medformer: A multi-granularity patching transformer for medical time-series classification,

Reference 21

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This paper cites Transfer learning in sensor- based human activity recognition: A survey,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Transfer learning in sensor- based human activity recognition: A survey,

Reference 22

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Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Foundation models defining a new era in vision: a survey and outlook,

Reference 23

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This paper cites Advancing domain-specific adaptationsoflargelanguagemodelsthroughtransfer learning and fine-tuning techniques: An analytical study,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Advancing domain-specific adaptationsoflargelanguagemodelsthroughtransfer learning and fine-tuning techniques: An analytical study,

Reference 24

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Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Recentadvancesoffoundationlanguage models-based continual learning: A survey,

Reference 25

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Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models

Reference 26

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Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics TimeGPT-1

Reference 27

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Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Lag-llama: Towards foundation models for time series forecasting,

Reference 28

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Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Visualizing data using t-sne.,

Reference 29

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This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 30

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This paper cites Understanding how dimension reduction tools work: An empirical approach to deciphering t-sne, umap, trimap, and pacmap for data visualization,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Understanding how dimension reduction tools work: An empirical approach to deciphering t-sne, umap, trimap, and pacmap for data visualization,

Reference 31

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Observation cec91ff8-3cc2-48c9-b6f0-3e460860a198 · outbound

This paper cites Combining dataminingandvisualization: Umaponrope,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Combining dataminingandvisualization: Umaponrope,

Reference 32

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This paper cites Dafted: Decoupled asymmetric fusion of tabular and echocardiographic data for cardiac hypertension diagnosis,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Dafted: Decoupled asymmetric fusion of tabular and echocardiographic data for cardiac hypertension diagnosis,

Reference 33

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Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Timecluster: dimension reduction applied to temporal data for visual analytics,

Reference 34

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This paper cites Human-in-the-loop: visual analytics for building models recognising behavioural patterns in time series,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Human-in-the-loop: visual analytics for building models recognising behavioural patterns in time series,

Reference 35

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Observation 40963667-d7de-4499-8437-78347284c33c · outbound

This paper cites tsai - a state-of-the-art deep learning library for time series and sequential data.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics tsai - a state-of-the-art deep learning library for time series and sequential data

Reference 36

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This paper cites Inceptiontime: Findingalexnetfor time series classification,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Inceptiontime: Findingalexnetfor time series classification,

Reference 37

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This paper cites STUMPY:APowerfulandScalablePython Library for Time Series Data Mining,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics STUMPY:APowerfulandScalablePython Library for Time Series Data Mining,

Reference 38

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This paper cites The web as a jungle: Non-linear dynamical systems for co-evolving online activities,.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics The web as a jungle: Non-linear dynamical systems for co-evolving online activities,

Reference 39

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Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics aeon: a python toolkitforlearningfromtimeseries,

Reference 40

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Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Soft-dtw: a differentiable loss function for time-series,

Reference 41

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Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Unresolved cited work

Reference 2001

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This paper cites Available: https://umap- learn.readthedocs.io/en/latest/interactive_viz.html, Accessed: 2024-06-26.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics Available: https://umap- learn.readthedocs.io/en/latest/interactive_viz.html, Accessed: 2024-06-26

Reference 2018

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Pith citing papers

No inbound Pith citation observations are available.