HyFAD: Hybrid Time-Frequency Diffusion with Frequency-Aware Embedding for Time Series Imputation
arXiv:2606.05239v1 Announce Type: cross Abstract: Diffusion models have demonstrated strong performance in time series modeling due to their ability to progressively capture complex data distributions through iterative denoising. However, existing approaches struggle with frequency-sensitive denoising, high-frequency reconstruction and balancing global trends with local dynamics. To address these limitations, we propose \textbf{HyFAD}, a \textbf{Hy}brid time-frequency \textbf{D}iffusion model with \textbf{F}requency-\textbf{A}ware embedding for time series imputation. Built upon the DDPM parad
The continuous advancements in AI, particularly diffusion models, are pushing researchers to address their current limitations in time series analysis, leading to innovations like HyFAD.
Improved time series imputation will lead to more accurate predictive models across various sectors, enhancing decision-making and operational efficiency for sophisticated readers.
The ability to accurately reconstruct high-frequency data and balance global trends with local dynamics in time series models is significantly improved, offering more robust analytical tools.
- · AI/ML researchers
- · Data scientists
- · Financial services
- · Healthcare
- · Traditional imputation methods
More accurate predictive analytics will become available for diverse applications such as stock market forecasting and medical diagnostics.
Industries reliant on time series data, like energy and logistics, will see improved operational planning and resource management.
The increased reliability of AI models could accelerate the broader adoption of autonomous systems that depend on dynamic data inputs.
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Read at arXiv cs.LG