ROAR: Random-Order Autoregressive Time Series Forecasting from a Visual Perspective

Autoregressive (AR) models have been increasingly applied to time series forecasting, owing to their favorable scalability and ability to accommodate prediction tasks of different horizons. However, most existing AR time series models are constrained by inflexible AR modeling paradigms and struggle with error accumulation. More general and flexible AR models and training methods for time series remain underexplored. To bridge this gap, we propose ROAR, a novel AR framework for time series forecasting. For AR modeling, motivated by the additional spatial structure introduced by the visual representation of time series, we reformulate forecasting as a random-order AR generation task on the time series image. For training, we devise a coupled training strategy that jointly supervises the entire backbone with signals from both teacher-forcing and free-running. These compact designs not only enable ROAR to learn richer contextual dependencies, but also narrow the training–inference mismatch, thereby alleviating the error accumulation incurred by the exposure bias. Comprehensive experiments conducted on 10 real-world benchmarks demonstrate that ROAR consistently achieves state-of-the-art performance while exhibiting superior robustness and generalization across multiple domains. Our code is available at: https://anonymous.4open.science/r/ROAR-6702