FIDE: Frequency-Inflated Conditional Diffusion Model for Extreme-Aware Time Series Generation

Asadullah Hill Galib, Pang-Ning Tan, Lifeng Luo

Keywords: Diffusion model, time series, extreme values

TL;DR: A frequency-inflated conditional diffusion model that enhances time series generation by preserving extreme values distribution

Abstract: Time series generation is a crucial aspect of data analysis, playing a pivotal role in learning the temporal patterns and their underlying dynamics across diverse fields. Conventional time series generation methods often struggle to capture extreme values adequately, diminishing their value in critical applications such as scenario planning and management for healthcare, finance, climate change adaptation, and beyond. In this paper, we introduce a conditional diffusion model called FIDE to address the challenge of preserving the distribution of extreme values in generative modeling for time series. FIDE employs a novel high-frequency inflation strategy in the frequency domain, preventing premature fade-out of the extreme value. It also extends traditional diffusion-based model, enabling the generation of samples conditioned on the block maxima, thereby enhancing the model's capacity to capture extreme events. Additionally, the FIDE framework incorporates the Generalized Extreme Value (GEV) distribution within its generative modeling framework, ensuring fidelity to both block maxima and overall data distribution. Experimental results on real-world and synthetic data showcase the efficacy of FIDE over baseline methods, highlighting its potential in advancing Generative AI for time series analysis, specifically in accurately modeling extreme events.

Primary Area: Machine learning for physical sciences (for example: climate, physics)

Submission Number: 12290