Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting (2206.04038v4)

Abstract: 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. Our code is publicly available in https://github.com/BorealisAI/scaleformer.

An Overview of Scaleformer: Iterative Multi-Scale Refining Transformers for Time Series Forecasting

Recent advances in transformer architectures have significantly enhanced time series forecasting. The paper "Scaleformer: Iterative Multi-Scale Refining Transformers for Time Series Forecasting," introduces an innovative framework that improves scale awareness in transformer-based models. This framework, known as Scaleformer, iteratively refines time series forecasts at multiple scales, thus providing substantial performance improvements over conventional transformers.

Methodology and Contributions

The Scaleformer framework is characterized by several core components:

1. Iterative Multi-Scale Refinement: This core innovation allows transformers to refine forecasts iteratively across different temporal scales. By employing a multi-scale refinement process, the model is better equipped to capture the complex interdependencies and dynamic characteristics inherent in time series data.
2. Cross-Scale Normalization: The normalization scheme is devised to mitigate distribution shifts that can occur during iterative refinements across scales. This is essential to preventing the error propagation that could otherwise undermine the forecasting accuracy.
3. Architecture Agnosticism: The framework is designed to integrate seamlessly with various existing transformer architectures, such as FEDformer, Autoformer, Informer, and others. This flexibility allows Scaleformer to serve as an orthogonal improvement layer to other model-specific advancements.

The paper empirically demonstrates the utility of Scaleformer through extensive evaluations on multiple datasets, achieving improvements in mean squared error ranging from 5.5% to 38.5% over baseline transformer models. These results are statistically significant across different forecasting tasks and signify a marked enhancement in both the trend-following capability and local variation capture of transformer-based models.

Detailed Analysis and Results

Experiments conducted on public datasets, including Electricity, Traffic, Weather, and Exchange Rate, reveal the robust applicability of Scaleformer's approach. A detailed ablation paper highlights that the iterative refinement process synergistically complements the adaptive loss function, a loss designed to be sensitive to outliers in data distribution, improving robustness.

Statistical tests corroborate the significance of Scaleformer's improvements, with p-values indicative of meaningful performance enhancements across numerous experimental setups. The multi-scale approach not only provides increased accuracy but also maintains computational efficiency, crucial for deploying models on large-scale data where resources might be a constraint.

Implications and Future Directions

Scaleformer's incorporation of multi-scale processing aligns with broader trends in cognitive computing that emphasize hierarchical and scale-aware data processing. From a theoretical perspective, its capability to iteratively refine forecasts mimics certain human recognition patterns, which might have implications for developing more human-like AI systems.

In practice, Scaleformer has broad potential applications, ranging from weather and financial forecasting to real-time demand prediction. The modularity of the approach also opens avenues for enhancing non-transformer models, as evidenced by successful trials with architectures such as NHits and FiLM.

Looking to the future, the Scaleformer framework can potentially be adapted to probabilistic forecasting methods, further broadening its applicability. Additionally, improvements in computational efficiency, such as optimizing normalization processes or leveraging interpolation at finer scales, could enhance the deployability of such models in even more resource-constrained environments.

In summary, the Scaleformer framework exemplifies a significant step forward in making transformer-based time series forecasting models more versatile and robust across various temporal dynamics. Its emphasis on iterative refinement and cross-scale normalization provides a promising avenue for further research and application in the field of AI-based forecasting.