Learning Spatio-Temporal Foundation Models from Pure Synthetic Data | Paper Citation Record

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This paper cites Foundation models for spatio-temporal data science: A tutorial and survey,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Foundation models for spatio-temporal data science: A tutorial and survey,

Reference 1

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This paper cites Unraveling spatio-temporal foundation models via the pipeline lens: A comprehensive review,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Unraveling spatio-temporal foundation models via the pipeline lens: A comprehensive review,

Reference 2

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This paper cites Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities

Reference 3

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This paper cites Spatio-temporal graph neural networks for predictive learning in urban computing: A survey,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Spatio-temporal graph neural networks for predictive learning in urban computing: A survey,

Reference 4

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This paper cites Flownet: Modeling dynamic spatio-temporal systems via flow propagation,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Flownet: Modeling dynamic spatio-temporal systems via flow propagation,

Reference 5

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This paper cites Representation learning for spatiotemporal physical systems,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Representation learning for spatiotemporal physical systems,

Reference 6

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This paper cites Hierarchical Planning with Latent World Models.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Hierarchical Planning with Latent World Models

Reference 7

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This paper cites An observed value consistent diffusion model for imputing missing values in multivariate time series,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data An observed value consistent diffusion model for imputing missing values in multivariate time series,

Reference 8

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

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Foundation models for time series analysis: A tutorial and survey,

Reference 9

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

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Deep learning for time series forecasting: Tutorial and literature survey,

Reference 10

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Observation a9deb03e-ae92-4e07-8fc4-35f48f90093f · outbound

This paper cites Time-series forecasting with deep learning: a survey,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Time-series forecasting with deep learning: a survey,

Reference 11

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This paper cites A survey on diffusion models for time series and spatio-temporal data,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data A survey on diffusion models for time series and spatio-temporal data,

Reference 12

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This paper cites A survey on diffusion models for time series and spatio-temporal data,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data A survey on diffusion models for time series and spatio-temporal data,

Reference 13

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This paper cites Diffstg: Probabilistic spatio-temporal graph forecasting with denoising diffusion models,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Diffstg: Probabilistic spatio-temporal graph forecasting with denoising diffusion models,

Reference 14

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Observation a1d20111-b362-43f5-8f9c-9456babec75f · outbound

This paper cites Towards unifying diffusion models for probabilistic spatio-temporal graph learning,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Towards unifying diffusion models for probabilistic spatio-temporal graph learning,

Reference 15

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This paper cites A comprehensive survey of regression-based loss functions for time series forecasting,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data A comprehensive survey of regression-based loss functions for time series forecasting,

Reference 16

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This paper cites Soft-dtw: a differentiable loss function for time-series,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Soft-dtw: a differentiable loss function for time-series,

Reference 17

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This paper cites Shape and time distortion loss for training deep time series forecasting models,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Shape and time distortion loss for training deep time series forecasting models,

Reference 18

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This paper cites Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

Reference 19

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This paper cites STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning

Reference 20

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

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data A decoder-only foundation model for time-series forecasting,

Reference 21

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

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Chronos: Learning the Language of Time Series

Reference 22

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This paper cites Unified training of universal time series forecasting transformers,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Unified training of universal time series forecasting transformers,

Reference 23

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

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 24

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Observation 08ba469b-7e7b-46fe-9ad6-4c5b35e1b261 · outbound

This paper cites Moirai 2.0: When less is more for time series forecasting,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Moirai 2.0: When less is more for time series forecasting,

Reference 25

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This paper cites Unitime: A language- empowered unified model for cross-domain time series forecasting,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Unitime: A language- empowered unified model for cross-domain time series forecasting,

Reference 26

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

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data MOMENT: A Family of Open Time-series Foundation Models

Reference 27

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

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 28

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This paper cites Opencity: Open spatio-temporal foundation models for traffic prediction,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Opencity: Open spatio-temporal foundation models for traffic prediction,

Reference 29

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This paper cites Unist: A prompt-empowered universal model for urban spatio-temporal prediction,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Unist: A prompt-empowered universal model for urban spatio-temporal prediction,

Reference 30

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This paper cites Urbangpt: Spatio-temporal large language models,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Urbangpt: Spatio-temporal large language models,

Reference 31

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This paper cites Airformer: Predicting nationwide air quality in china with transformers,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Airformer: Predicting nationwide air quality in china with transformers,

Reference 32

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This paper cites Spatio-temporal field neural networks for air quality inference,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Spatio-temporal field neural networks for air quality inference,

Reference 33

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This paper cites Accurate medium-range global weather forecasting with 3d neural networks,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Accurate medium-range global weather forecasting with 3d neural networks,

Reference 34

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This paper cites ClimaX: A foundation model for weather and climate.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data ClimaX: A foundation model for weather and climate

Reference 35

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This paper cites Hyper-Connections.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Hyper-Connections

Reference 36

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

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Reversible instance normalization for accurate time-series forecasting against distribution shift,

Reference 37

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This paper cites Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 38

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This paper cites Roformer: Enhanced transformer with rotary position embedding,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Roformer: Enhanced transformer with rotary position embedding,

Reference 39

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This paper cites Structured Sequence Modeling with Graph Convolutional Recurrent Networks.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Structured Sequence Modeling with Graph Convolutional Recurrent Networks

Reference 40

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This paper cites Principal component analysis,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Principal component analysis,

Reference 41

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This paper cites Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting,

Reference 42

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This paper cites Diffusion convolutional recurrent neural network: Data- driven traffic forecasting,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Diffusion convolutional recurrent neural network: Data- driven traffic forecasting,

Reference 43

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This paper cites Learning to factorize spatio-temporal foundation models,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Learning to factorize spatio-temporal foundation models,

Reference 44

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This paper cites Rose: Register-assisted general time series forecasting with decomposed frequency learning,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Rose: Register-assisted general time series forecasting with decomposed frequency learning,

Reference 45

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This paper cites Decoupled Weight Decay Regularization.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Decoupled Weight Decay Regularization

Reference 46

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This paper cites Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

Reference 47

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This paper cites Largest: A benchmark dataset for large-scale traffic forecasting,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Largest: A benchmark dataset for large-scale traffic forecasting,

Reference 48

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This paper cites Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach

Reference 49

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This paper cites Lade: The first comprehensive last-mile express dataset from industry,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Lade: The first comprehensive last-mile express dataset from industry,

Reference 50

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This paper cites Generative adversarial networks for spatio-temporal data: A survey,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Generative adversarial networks for spatio-temporal data: A survey,

Reference 51

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This paper cites Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs

Reference 52

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This paper cites Difftraj: Generating gps trajectory with diffusion probabilistic model,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Difftraj: Generating gps trajectory with diffusion probabilistic model,

Reference 53

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This paper cites Controltraj: Controllable trajectory generation with topology-constrained diffusion model,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Controltraj: Controllable trajectory generation with topology-constrained diffusion model,

Reference 54

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This paper cites Causal Time Series Generation via Diffusion Models.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Causal Time Series Generation via Diffusion Models

Reference 55

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This paper cites Self-supervised learning for time series analysis: Taxonomy, progress, and prospects,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Self-supervised learning for time series analysis: Taxonomy, progress, and prospects,

Reference 56

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This paper cites Self-supervised learning from images with a joint-embedding predictive architecture,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Self-supervised learning from images with a joint-embedding predictive architecture,

Reference 57

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This paper cites LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Reference 58

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This paper cites T-jepa: A joint-embedding predictive architecture for trajectory similarity computation,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data T-jepa: A joint-embedding predictive architecture for trajectory similarity computation,

Reference 59

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This paper cites HiT-JEPA: A Hierarchical Self-supervised Trajectory Embedding Framework for Similarity Computation.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data HiT-JEPA: A Hierarchical Self-supervised Trajectory Embedding Framework for Similarity Computation

Reference 60

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This paper cites Towards Neural Scaling Laws for Time Series Foundation Models.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Towards Neural Scaling Laws for Time Series Foundation Models

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This paper cites Scaling law for time series forecasting,.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Scaling law for time series forecasting,

Reference 62

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This paper cites Scaling-laws for Large Time-series Models.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Scaling-laws for Large Time-series Models

Reference 63

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Observation 77ebc6f0-70df-4f1e-aa68-487695e33c88 · outbound

This paper cites sim-to-real.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data sim-to-real

Reference 64

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