Spectral Attention for Improved Long-Range Dependency Modeling in Time Series Forecasting

Conceptos Básicos

This paper introduces Spectral Attention, a novel mechanism that enhances time series forecasting models by enabling them to effectively capture and utilize long-range dependencies in sequential data, leading to improved prediction accuracy.

Resumen

• Bibliographic Information: Kang, B. G., Lee, D., Kim, H., Chung, D., & Yoon, S. (2024). Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting. Advances in Neural Information Processing Systems, 38.

• Research Objective: This paper aims to address the limitations of existing time series forecasting (TSF) models in capturing long-range dependencies, a crucial aspect for accurate predictions.

• Methodology: The authors propose Spectral Attention (SA), a plug-in module that can be integrated into various TSF models. SA leverages exponential moving averages (EMAs) with multiple smoothing factors to capture long-range trends and utilizes an attention mechanism to learn the importance of different frequency components for prediction. They further extend SA to Batched Spectral Attention (BSA) for efficient parallel training and enhanced gradient flow across time steps.

• Key Findings: The study demonstrates that BSA consistently improves the performance of seven state-of-the-art TSF models across eleven real-world datasets. The authors show that BSA effectively captures long-range dependencies beyond the look-back window, leading to significant performance gains, particularly in long-term forecasting tasks.

• Main Conclusions: The paper concludes that BSA offers a model-agnostic solution for enhancing long-range dependency modeling in TSF. The authors suggest that BSA's ability to capture both short-term and long-term patterns makes it a promising approach for improving the accuracy of real-world applications.

• Significance: This research significantly contributes to the field of time series forecasting by introducing a novel and effective method for addressing the long-standing challenge of capturing long-range dependencies. The proposed BSA module has the potential to enhance the performance of various TSF models and improve the accuracy of predictions in diverse applications.

• Limitations and Future Research: The study acknowledges limitations in exploring the optimal placement of BSA within different base models. Additionally, the authors suggest further investigation into the effectiveness of BSA on datasets with predominantly high-frequency information.

Estadísticas

BSA improved MSE by 0.96% to 7.2% across different architectures.
Linear-based models showed particularly high-performance improvements with BSA.
BSA led to statistically significant improvements in 82% of the datasets tested.
Using a smoothing factor of 0.999 in the EMA allows BSA to preserve trends over 6,000 time steps.

Citas

"To overcome these limitations, we propose Spectral Attention, which can be applied to most TSF models and enables the model to utilize long-range temporal correlations in sequentially obtained training data."
"Our approach preserves the base TSF model architecture and learning objective while enabling the model to leverage long-term trends spanning thousands of steps."
"Batched Spectral Attention demonstrates consistent model-agnostic performance improvements, particularly showcasing superior performance on datasets with significant long-term trend variations."

Ideas clave extraídas de

Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting

by Bong Gyun Ka... a las arxiv.org 10-29-2024

https://arxiv.org/pdf/2410.20772.pdf