Paper Citation Record · LEDGER

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization

As of 16 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 6 inbound Pith citation observations for arXiv:2412.05244.

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

pith.paper-citation-record.v1
2412.05244 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

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measured 71 of 71 standing notices

One-hop event checks from named stored sources.

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measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:25:59.022785Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:16:24.872523Z

Reference resolution

65 of 65 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bfae2da3-5a9d-4938-b6ff-43d489c7c47d · outbound

This paper cites write newline.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization write newline

Reference 1

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This paper cites @esa (Ref.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization @esa (Ref

Reference 2

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Unresolved cited work

Reference 3

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This paper cites L օx1?7 VI<5F mL G,+@ T jAF\.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization L օx1?7 VI<5F mL G,+@ T jAF\

Reference 4

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This paper cites Adaptive thresholding of wavelet coefficients.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Adaptive thresholding of wavelet coefficients

Reference 5

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Observation cad3928e-1090-4e1b-8ac9-a835c5491e66 · outbound

This paper cites Gluonts: Probabilistic and neural time series modeling in python.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Gluonts: Probabilistic and neural time series modeling in python

Reference 6

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Observation 454df075-864a-4581-9bf1-9244093d4f54 · outbound

This paper cites Evaluating various tokenizers for arabic text classification.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Evaluating various tokenizers for arabic text classification

Reference 7

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Observation 672e647c-17f4-463a-81c4-ee66a9c31797 · outbound

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

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Chronos: Learning the Language of Time Series

Reference 8

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Observation 585b1c08-040a-4e70-9858-e70a24a7f1e0 · outbound

This paper cites Deep learning for time series forecasting: Tutorial and literature survey.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Deep learning for time series forecasting: Tutorial and literature survey

Reference 9

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This paper cites Language Models are Few-Shot Learners.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Language Models are Few-Shot Learners

Reference 10

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This paper cites Adaptive wavelet thresholding for image denoising and compression.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Adaptive wavelet thresholding for image denoising and compression

Reference 11

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Observation 6ca48746-29de-4441-9d86-51a816c56e97 · outbound

This paper cites ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning

Reference 12

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This paper cites The jpeg2000 still image coding system: an overview.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization The jpeg2000 still image coding system: an overview

Reference 13

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Observation e5823339-78c0-4c05-86f1-5aee30d9b8a0 · outbound

This paper cites Getting the most out of your tokenizer for pre-training and domain adaptation.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Getting the most out of your tokenizer for pre-training and domain adaptation

Reference 14

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

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization A decoder-only foundation model for time-series forecasting

Reference 15

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This paper cites Ten lectures on wavelets.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Ten lectures on wavelets

Reference 16

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This paper cites De-noising by soft-thresholding.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization De-noising by soft-thresholding

Reference 17

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Observation 66a58b7a-9ba3-4fc4-a392-7ef74582f6e3 · outbound

This paper cites On the histogram as a density estimator: L 2 theory.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization On the histogram as a density estimator: L 2 theory

Reference 18

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This paper cites A new algorithm for data compression.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization A new algorithm for data compression

Reference 19

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This paper cites Statsforecast: Lightning fast forecasting with statistical and econometric models.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Statsforecast: Lightning fast forecasting with statistical and econometric models

Reference 20

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This paper cites xVal: A Continuous Numerical Tokenization for Scientific Language Models.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization xVal: A Continuous Numerical Tokenization for Scientific Language Models

Reference 21

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This paper cites Masked particle modeling on sets: Towards self-supervised high energy physics foundation models.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Masked particle modeling on sets: Towards self-supervised high energy physics foundation models

Reference 22

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Observation 25a1215d-969f-4121-b8ed-a4fba199ece8 · outbound

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

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization MOMENT: A Family of Open Time-series Foundation Models

Reference 23

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Large language models are zero-shot time series forecasters

Reference 24

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Wavelet score-based generative modeling

Reference 25

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Zur theorie der orthogonalen funktionensysteme

Reference 26

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This paper cites Another look at measures of forecast accuracy.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Another look at measures of forecast accuracy

Reference 27

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

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 28

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Quantile regression

Reference 29

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This paper cites An experimental review on deep learning architectures for time series forecasting.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization An experimental review on deep learning architectures for time series forecasting

Reference 30

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Shape and time distortion loss for training deep time series forecasting models

Reference 31

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Temporal fusion transformers for interpretable multi-horizon time series forecasting

Reference 32

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization A Wavelet Tour of Signal Processing: The Sparse Way

Reference 33

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Multiresolution approximations and wavelet orthonormal bases of L ^2( R )

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Wavelets Are All You Need for Autoregressive Image Generation

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Wavelets and operators: volume 1

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Large Language Models as General Pattern Machines

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This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Oppenheim and Ronald W

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Adaptive, hands-off stream mining

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Difffind: Discovering differential equations from time series

Reference 41

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Deep learning for audio signal processing

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Exploring the limits of transfer learning with a unified text-to-text transformer

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Reference 44

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Deepar: Probabilistic forecasting with autoregressive recurrent networks

Reference 45

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization W-transformers: a wavelet-based transformer framework for univariate time series forecasting

Reference 46

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Wavelets and filter banks

Reference 48

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Towards foundation models for scientific machine learning: Characterizing scaling and transfer behavior

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis

Reference 50

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

Reference 51

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Juman++: A morphological analysis toolkit for scriptio continua

Reference 52

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization A practical guide to wavelet analysis

Reference 53

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Observation e443e02a-98f8-41da-86e7-4b66a42eceae · outbound

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 54

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Observation 47adb47b-7850-4b3b-8748-66cc16a8a803 · outbound

This paper cites A tutorial on modern lossy wavelet image compression: foundations of jpeg 2000.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization A tutorial on modern lossy wavelet image compression: foundations of jpeg 2000

Reference 55

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Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Neural discrete representation learning

Reference 56

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Observation 4a818756-b45f-49dc-9566-248ca698f488 · outbound

This paper cites Wavelets and subband coding.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Wavelets and subband coding

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:39.482979Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7accfd3c-3fe8-4f5f-872b-ef356e3e6a7f · outbound

This paper cites Unified Training of Universal Time Series Forecasting Transformers.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Unified Training of Universal Time Series Forecasting Transformers

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T20:54:38.980289Z

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Observation da0475cd-a5e6-4416-8cb5-e68e7ad96ab5 · outbound

This paper cites Promptcast: A new prompt-based learning paradigm for time series forecasting.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Promptcast: A new prompt-based learning paradigm for time series forecasting

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T20:54:38.984966Z

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Observation f831e2a7-687b-444d-bb6a-fe664237c428 · outbound

This paper cites Soundstream: An end-to-end neural audio codec.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Soundstream: An end-to-end neural audio codec

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T20:54:38.989198Z

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Observation dd86dce0-1715-4804-84d4-50c0db8ce56e · outbound

This paper cites First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T20:54:38.992694Z

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Observation 322f77bc-e3d4-48a4-b65f-3cfc4f4902f8 · outbound

This paper cites Large Language Models for Time Series: A Survey.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Large Language Models for Time Series: A Survey

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T20:54:38.996220Z

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Observation a2f938d3-e53a-4b49-b47a-21c99ae12d06 · outbound

This paper cites Outline, then details: Syntactically guided coarse-to-fine code generation.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Outline, then details: Syntactically guided coarse-to-fine code generation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:54:39.451713Z

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Observation 3467e9fd-7c05-4bc8-b377-d6517d8032bc · outbound

This paper cites Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T20:54:39.003802Z

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Observation 0639ce56-4d09-4266-870a-90666f7be21a · outbound

This paper cites Wavelet-Based Image Tokenizer for Vision Transformers.

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization Wavelet-Based Image Tokenizer for Vision Transformers

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T20:54:39.007515Z

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Pith citing papers

Observation 7c8140d2-5233-49af-b966-128e7669f0ce · inbound

OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed Domain cites this paper.

OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed Domain Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization

Reference 2025

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Observation 5267a702-fa7c-400e-b976-ee51c5c81c15 · inbound

What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies cites this paper.

What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization

Reference 66

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Observation 9b2043d4-cc36-40d8-b012-f7e1a6c46d87 · inbound

Spectral Vision Transformer for Efficient Tokenization with Limited Data cites this paper.

Spectral Vision Transformer for Efficient Tokenization with Limited Data Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:57:28.171802Z

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Observation 51a3e9e1-c534-4b5b-8ca0-0691b5d6975c · inbound

Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures cites this paper.

Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:16:24.874506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f71556e7-3ca9-4774-b036-d1733334541d · inbound

Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures cites this paper.

Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-12T15:18:09.444513Z

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Observation 1c3eab99-d0e2-47f9-a5e6-04eae20799dd · inbound

JEPA for AI-Native 6G: Predictive Representations and Open Challenges cites this paper.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

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