Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting

The performance of time series forecasting has recently been greatly improved by the introduction of transformers. In this paper, we propose a general multi-scale framework that can be applied to the state-of-the-art transformer-based time series forecasting models (FEDformer, Autoformer, etc.). By iteratively refining a forecasted time series at multiple scales with shared weights, introducing architecture adaptations, and a specially-designed normalization scheme, we are able to achieve significant performance improvements, from 5.5% to 38.5% across datasets and transformer architectures, with minimal additional computational overhead. Via detailed ablation studies, we demonstrate the effectiveness of each of our contributions across the architecture and methodology. Furthermore, our experiments on various public datasets demonstrate that the proposed improvements outperform their corresponding baseline counterparts. The essential Figure 1: Intermediate forecasts by our model cross-scale feature relationships are often learnt implicitly, at different time scales. Iterative refinement and are not encouraged by architectural priors of a time series forecast is a strong structural of any kind beyond the stacked attention blocks that prior that benefits time series forecasting. Autoformer (Xu et al., 2021) and Fedformer (Zhou et al., 2022b) introduced some emphasis on scale-awareness by enforcing different computational paths for the trend and seasonal components of the input time series; however, this structural prior only focused on two scales: low-and high-frequency components. Given their importance to forecasting, can we make transformers more scale-aware? We enable this scale-awareness with Scaleformer. In our proposed approach, showcased in Figure 1, time series forecasts are iteratively refined at successive time-steps, allowing the model to better capture the inter-dependencies and specificities of each scale. However, scale itself is not sufficient.