DLTTS: Diffusion Model for Long-Tailed Time Series Generation in Industrial Scenarios

Industrial time series analysis is the core foundation of equipment status monitoring and industrial intelligence. However, the long-tailed distribution characteristics of time series caused by low-frequency and low-probability events seriously restrict the performance of analysis models. In key scenarios such as industrial process prediction and anomaly detection, data analysis models face obvious performance bottlenecks due to insufficient representation of tail events. Existing data augmentation methods have dual limitations in capturing tail patterns and modeling long-distance time series dependencies. To address this challenge, this paper proposes an industrial long-tailed time series generator (DLTTS) model based on a diffusion model. Firstly, a hybrid architecture generation model is constructed to deeply integrate the encoder-decoder informer structure with the traditional diffusion process to maintain long-distance time patterns and achieve long-distance time series output; Secondly, we propose Fourier-based batch-Monte Carlo (FBMC) loss to enhance the model's ability to capture low-frequency events, thereby improve the quality of tail time series generation. Experiments show that DLTTS maintains the authenticity and diversity of tail time series generation in industrial-grade long-tail time series generation tasks. It also exhibits robust performance in imputation and forecasting tasks, verifying the multiple performance advantages of this method for cross-task application.