Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions | Paper Citation Record

Observation a566bdc5-0f38-4cbc-ade6-d20524f05791 · outbound

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

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Chronos: Learning the language of time series

Reference 1

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Observation 70c1466b-8666-4691-b0b8-f6c45601fca1 · outbound

This paper cites ViViT: A Video Vision Transformer.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions ViViT: A Video Vision Transformer

Reference 2

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This paper cites Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Reference 3

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This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representations.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions wav2vec 2.0: A framework for self-supervised learning of speech representations

Reference 4

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

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions The UEA multivariate time series classification archive, 2018

Reference 5

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

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 6

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This paper cites A Cookbook of Self-Supervised Learning.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions A Cookbook of Self-Supervised Learning

Reference 7

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This paper cites MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features

Reference 8

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This paper cites Revisiting Feature Prediction for Learning Visual Representations from Video.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Revisiting Feature Prediction for Learning Visual Representations from Video

Reference 9

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This paper cites Language models are few-shot learners.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Language models are few-shot learners

Reference 10

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This paper cites Learning contrastive embedding in low-dimensional space.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Learning contrastive embedding in low-dimensional space

Reference 11

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This paper cites A simple framework for contrastive learning of visual representations.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions A simple framework for contrastive learning of visual representations

Reference 12

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Debiased contrastive learning

Reference 13

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

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions A decoder-only foundation model for time-series forecasting

Reference 14

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 15

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions BERT : Pre-training of deep bidirectional transformers for language understanding

Reference 16

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This paper cites Deep learning for event-driven stock prediction.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Deep learning for event-driven stock prediction

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This paper cites Speech-transformer: A no-recurrence sequence-to-sequence model for speech recognition.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Speech-transformer: A no-recurrence sequence-to-sequence model for speech recognition

Reference 18

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 19

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This paper cites Video Representation Learning with Joint-Embedding Predictive Architectures.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Video Representation Learning with Joint-Embedding Predictive Architectures

Reference 20

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This paper cites PyTorch Lightning , March 2019.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions PyTorch Lightning , March 2019

Reference 21

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This paper cites T-Rep : Representation learning for time series using time-embeddings.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions T-Rep : Representation learning for time series using time-embeddings

Reference 22

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This paper cites Unsupervised scalable representation learning for multivariate time series.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Unsupervised scalable representation learning for multivariate time series

Reference 23

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

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions UniTS: A Unified Multi-Task Time Series Model

Reference 24

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Understanding the difficulty of training deep feedforward neural networks

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions AST: Audio Spectrogram Transformer

Reference 26

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

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions MOMENT: A Family of Open Time-series Foundation Models

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Bootstrap your own latent-a new approach to self-supervised learning

Reference 28

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Bootstrap your own latent: A new approach to self-supervised Learning

Reference 29

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This paper cites Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

Reference 30

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 31

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention

Reference 32

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Neural Machine Translation in Linear Time

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Katser and Vyacheslav O

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions A comprehensive survey of deep learning for time series forecasting: Architectural diversity and open challenges

Reference 35

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Segment anything

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions A path towards autonomous machine intelligence

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 38

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Temporal convolutional attention neural networks for time series forecasting

Reference 39

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions iTransformer : Inverted transformers are effective for time series forecasting

Reference 40

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Unresolved cited work

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Nomic embed: Training a reproducible long context text embedder

Reference 42

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions 2 OLMo 2 Furious

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions DINOv2: Learning Robust Visual Features without Supervision

Reference 44

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions On the difficulty of training recurrent neural networks

Reference 45

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 46

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Improving language understanding by generative pre-training

Reference 47

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Learning transferable visual models from natural language supervision

Reference 48

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions DeepAR : Probabilistic forecasting with autoregressive recurrent networks

Reference 49

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Roformer: Enhanced transformer with rotary position embedding

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Machine learning for predictive maintenance: A multiple classifier approach

Reference 51

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures

Reference 52

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Unsupervised representation learning for time series with temporal neighborhood coding

Reference 53

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This paper cites Universal Time-Series Representation Learning: A Survey.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Universal Time-Series Representation Learning: A Survey

Reference 54

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Gomez, ukasz Kaiser, and Illia Polosukhin

Reference 55

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This paper cites TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems

Reference 56

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Unified training of universal time series forecasting transformers

Reference 57

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Observation 09147de2-80dd-4d9c-a16f-bdcdacb91944 · outbound

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

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:41.400881Z

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions TS2Vec : Towards universal representation of time series

Reference 59

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This paper cites Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:41.155697Z

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This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 61

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This paper cites Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Reference 62

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

Reference 63

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions write newline

Reference 64

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This paper cites @esa (Ref.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions @esa (Ref

Reference 65

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions Unresolved cited work

Reference 66

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Observation a2d401c2-3cd8-4940-9643-ddc997643626 · outbound

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Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions TimeGPT-1

Reference 67

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