  Recent studies have demonstrated the great power of Transformer models for
time series forecasting. One of the key elements that lead to the transformer's
success is the channel-independent (CI) strategy to improve the training
robustness. However, the ignorance of the correlation among different channels
in CI would limit the model's forecasting capacity. In this work, we design a
special Transformer, i.e., Channel Aligned Robust Blend Transformer (CARD for
short), that addresses key shortcomings of CI type Transformer in time series
forecasting. First, CARD introduces a channel-aligned attention structure that
allows it to capture both temporal correlations among signals and dynamical
dependence among multiple variables over time. Second, in order to efficiently
utilize the multi-scale knowledge, we design a token blend module to generate
tokens with different resolutions. Third, we introduce a robust loss function
for time series forecasting to alleviate the potential overfitting issue. This
new loss function weights the importance of forecasting over a finite horizon
based on prediction uncertainties. Our evaluation of multiple long-term and
short-term forecasting datasets demonstrates that CARD significantly
outperforms state-of-the-art time series forecasting methods. The code is
available at the following repository:https://github.com/wxie9/CARD
