  Predicting multivariate time series is crucial, demanding precise modeling of
intricate patterns, including inter-series dependencies and intra-series
variations. Distinctive trend characteristics in each time series pose
challenges, and existing methods, relying on basic moving average kernels, may
struggle with the non-linear structure and complex trends in real-world data.
Given that, we introduce a learnable decomposition strategy to capture dynamic
trend information more reasonably. Additionally, we propose a dual attention
module tailored to capture inter-series dependencies and intra-series
variations simultaneously for better time series forecasting, which is
implemented by channel-wise self-attention and autoregressive self-attention.
To evaluate the effectiveness of our method, we conducted experiments across
eight open-source datasets and compared it with the state-of-the-art methods.
Through the comparison results, our Leddam (LEarnable Decomposition and Dual
Attention Module) not only demonstrates significant advancements in predictive
performance, but also the proposed decomposition strategy can be plugged into
other methods with a large performance-boosting, from 11.87% to 48.56% MSE
error degradation.
