Time series forecasting task predicts future trends based on historical
information. Recent U-Net-based methods have demonstrated superior performance
in predicting real-world datasets. However, the performance of these models is
lower than patch-based models or linear models. In this work, we propose a
symmetric and hierarchical framework, Kernel-U-Net, which cuts the input
sequence into slices at each layer of the network and then computes them using
kernels. Furthermore, it generalizes the concept of convolutional kernels in
classic U-Net to accept custom kernels that follow the same design pattern.
Compared to the existing linear or transformer-based solution, our model
contains 3 advantages: 1) A small number of parameters: the parameters size is
$O(log(L)^2)$ where $L$ is the look-back window size, 2) Flexibility: its
kernels can be customized and fitted to the datasets, 3) Computation
efficiency: the computation complexity of transformer modules is reduced to
$O(log(L)^2)$ if they are placed close to the latent vector. Kernel-U-Net
accuracy was greater than or equal to the state-of-the-art model on six (out of
seven) real-world datasets.

时间序列预测任务基于历史信息预测未来趋势。本文提出了一种对称和分层的框架，Kernel-U-Net，它在网络的每一层将输入序列切分成片段，然后使用内核对其进行计算。与现有的线性或 Transformer 解决方案相比，我们的模型具有三个优势：1）参数较小，2）灵活性，3）计算效率。在六个（七个中的五个）真实数据集上，Kernel-U-Net 的准确性大于或等于最先进模型的准确性。