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Modelling time-series data generation with diffusion models for triaxial data
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DOI：10.1016/j.asoc.2025.114195.png)
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Abstract
En 中文
• A methodology using diffusion models and Modified Recurrence Plots to generate synthetic time-series data. • Generation of synthetic data from triaxial sensor signals for activity classification, addressing data scarcity issues. • Promising results in model performance stability using synthetic sensor data for out-of-distribution generalization. • Open-source code for further exploration in synthetic time-series generation and activity recognition tasks.
Keywords:
Diffusion models
Time series
Synthetic data
Data generation
Time series generation
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