TED-ITS: Tail-Enhanced Conditional Diffusion Fine-tuning for Label-Free Industrial Time-Series Generation

Diffusion models have achieved strong performance in time-series forecasting, missing-value imputation, and generation. However, real-world industrial time series often exhibit long-tailed distributions, where abundant normal samples dominate rare fault patterns and degrade the modeling of minority faults. Although class-conditional diffusion models alleviate this issue through semantic class guidance, two challenges remain. First, rare fault samples provide insufficient supervision for learning class-specific denoising dynamics, while increasing diffusion noise progressively obscures fault-specific characteristics. Second, class-conditional diffusion relies on explicit labels, which are generally unavailable from historical or incomplete observations in forecasting and imputation, leading to a mismatch between training and inference. To address these challenges, we propose TED-ITS, a tail-enhanced fine-tuning framework built upon a pretrained class-conditional diffusion model. TED-ITS introduces Noise-Adaptive Target Reconstruction (NATR), which adaptively constructs tail-class reconstruction targets according to noise-aware trajectory similarity and class frequency to strengthen minority fault supervision. It further incorporates Balanced-Condition Regularization (BCR), which samples auxiliary labels from a balanced distribution and enforces time- and frequency-domain consistency across different class conditions, thereby reducing reliance on explicit labels. Extensive experiments on multiple industrial benchmarks demonstrate that TED-ITS improves minority fault modeling and consistently achieves superior performance on downstream forecasting and imputation tasks compared with existing methods.