Time series forecasting has become an increasingly popular research area due
to its critical applications in various real-world domains such as traffic
management, weather prediction, and financial analysis. Despite significant
advancements, existing models face notable challenges, including the necessity
of manual hyperparameter tuning for different datasets, and difficulty in
effectively distinguishing signal from redundant features in data characterized
by strong seasonality. These issues hinder the generalization and practical
application of time series forecasting models. To solve this issues, we propose
an innovative time series forecasting model TimeSieve designed to address these
challenges. Our approach employs wavelet transforms to preprocess time series
data, effectively capturing multi-scale features without the need for
additional parameters or manual hyperparameter tuning. Additionally, we
introduce the information bottleneck theory that filters out redundant features
from both detail and approximation coefficients, retaining only the most
predictive information. This combination reduces significantly improves the
model's accuracy. Extensive experiments demonstrate that our model outperforms
existing state-of-the-art methods on 70\% of the datasets, achieving higher
predictive accuracy and better generalization across diverse datasets. Our
results validate the effectiveness of our approach in addressing the key
challenges in time series forecasting, paving the way for more reliable and
efficient predictive models in practical applications. The code for our model
is available at this https URL

我们提出了一种创新的时间序列预测模型 TimeSieve，通过使用小波变换预处理时间序列数据并引入信息瓶颈理论过滤冗余特征，提高了预测准确性和泛化能力，解决了时间序列预测中的关键问题。

时间筛：通过信息瓶颈提取时间动态

TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks

Modern single image super-resolution (SISR) system based on convolutional
neural networks (CNNs) achieves fancy performance while requires huge
computational costs. The problem on feature redundancy is well studied in
visual recognition task, but rarely discussed in SISR. Based on the observation
that many features in SISR models are also similar to each other, we propose to
use shift operation to generate the redundant features (i.e., ghost features).
Compared with depth-wise convolution which is time-consuming on GPU-like
devices, shift operation can bring a practical inference acceleration for CNNs
on common hardwares. We analyze the benefits of shift operation on SISR task
and make the shift orientation learnable based on Gumbel-Softmax trick.
Besides, a clustering procedure is explored based on pre-trained models to
identify the intrinsic filters for generating intrinsic features. The ghost
features will be derived by moving these intrinsic features along a specific
orientation. Finally, the complete output features are constructed by
concatenating the intrinsic and ghost features together. Extensive experiments
on several benchmark models and datasets demonstrate that both the non-compact
and lightweight SISR models embedded with the proposed method can achieve a
comparable performance to that of their baselines with a large reduction of
parameters, FLOPs and GPU inference latency. For instance, we reduce the
parameters by 46%, FLOPs by 46% and GPU inference latency by 42% of $\times2$
EDSR network with basically lossless performance.

本论文提出利用移位操作来生成冗余特征并构造完整特征，在多个基准模型和数据集上进行了广泛实验，结果显示所提出的方法嵌入了非紧凑和轻量级 SISR 模型，与基线模型相比，在大幅减少参数、FLOPs 和 GPU 推理延迟的同时，性能基本不变。