  Various deep learning models, especially some latest Transformer-based
approaches, have greatly improved the state-of-art performance for long-term
time series forecasting.However, those transformer-based models suffer a severe
deterioration performance with prolonged input length, which prohibits them
from using extended historical info.Moreover, these methods tend to handle
complex examples in long-term forecasting with increased model complexity,
which often leads to a significant increase in computation and less robustness
in performance(e.g., overfitting). We propose a novel neural network
architecture, called TreeDRNet, for more effective long-term forecasting.
Inspired by robust regression, we introduce doubly residual link structure to
make prediction more robust.Built upon Kolmogorov-Arnold representation
theorem, we explicitly introduce feature selection, model ensemble, and a tree
structure to further utilize the extended input sequence, which improves the
robustness and representation power of TreeDRNet. Unlike previous deep models
for sequential forecasting work, TreeDRNet is built entirely on multilayer
perceptron and thus enjoys high computational efficiency. Our extensive
empirical studies show that TreeDRNet is significantly more effective than
state-of-the-art methods, reducing prediction errors by 20% to 40% for
multivariate time series. In particular, TreeDRNet is over 10 times more
efficient than transformer-based methods. The code will be released soon.
