MetaIndux-TS: Frequency-Aware AIGC Foundation Model for Industrial Time Series

Implementing advanced AI techniques in industrial manufacturing requires large volumes of annotated sensor data. Unfortunately, collecting such data is often impractical due to extreme environments and the manual burden of expert annotation. Recent advancements in artificial intelligence generated content (AIGC) have inspired the exploration of industrial time-series generation to mitigate data shortages. However, existing AIGC models encounter difficulties in generating industrial time series due to their complex temporal dynamics, multichannel intercolumn correlations, and diverse frequency characteristics. To address these challenges, we propose MetaIndux-TS, a frequency-informed AIGC foundation model based on diffusion model frameworks. This model is designed to generate industrial time-series data under a variety of working conditions, across different types of equipment, and with variable lengths. Specifically, MetaIndux-TS integrates dual-frequency cross-attention networks, transforming time series into the frequency domain to model multivariate dependencies and capture intricate temporal details. In addition, the contrastive synthesis layer is constructed to generate high-fidelity time series by comparing periodic and long-term trends with initial noisy sequences. Comprehensive experiments show that MetaIndux-TS outperforms state-of-the-art models (SSSD, Dit, and TabDDPM), achieving a 57.5% improvement in fidelity and 20.4% in predictive score. MetaIndux-TS exhibits zero-shot generation capabilities for samples under unseen conditions, offering the potential to address data collection challenges in extreme environments. Codes are available at: https://github.com/Dolphin-wang/MetaIndux.