SynHAT uses a novel two-stage spatio-temporal diffusion framework with Latent Spatio-Temporal U-Net to synthesize realistic human activity traces, outperforming baselines by 52% on spatial and 33% on temporal metrics across four cities.
A survey on diffusion models for time series and spatio-temporal data,
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MMAF-guided learning constrains neural network training with Ornstein-Uhlenbeck process structure to generate calibrated spatio-temporal ensemble forecasts, where shallow feed-forward networks perform comparably to or better than convolutional or diffusion models.
CaTSG is a unified diffusion model for causal time series generation that handles observational, interventional, and counterfactual tasks via backdoor adjustment and abduction-action-prediction.
InsTraj generates realistic, instruction-faithful GPS trajectories by using an LLM to parse natural-language travel intent and a multimodal diffusion transformer to produce the paths.
AutoPV applies neural architecture search with a custom search space drawn from time series forecasting and photovoltaic models to automatically produce architectures that outperform predefined state-of-the-art models on a Chinese solar station dataset.
citing papers explorer
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SynHAT: A Two-stage Coarse-to-Fine Diffusion Framework for Synthesizing Human Activity Traces
SynHAT uses a novel two-stage spatio-temporal diffusion framework with Latent Spatio-Temporal U-Net to synthesize realistic human activity traces, outperforming baselines by 52% on spatial and 33% on temporal metrics across four cities.
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Spatio-temporal probabilistic forecast using MMAF-guided learning
MMAF-guided learning constrains neural network training with Ornstein-Uhlenbeck process structure to generate calibrated spatio-temporal ensemble forecasts, where shallow feed-forward networks perform comparably to or better than convolutional or diffusion models.
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Causal Time Series Generation via Diffusion Models
CaTSG is a unified diffusion model for causal time series generation that handles observational, interventional, and counterfactual tasks via backdoor adjustment and abduction-action-prediction.
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InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories
InsTraj generates realistic, instruction-faithful GPS trajectories by using an LLM to parse natural-language travel intent and a multimodal diffusion transformer to produce the paths.
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AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model
AutoPV applies neural architecture search with a custom search space drawn from time series forecasting and photovoltaic models to automatically produce architectures that outperform predefined state-of-the-art models on a Chinese solar station dataset.