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 https://github.com/xll0328/TimeSieve.

翻译：时间序列预测因其在交通管理、天气预测和金融分析等现实领域的关键应用，已成为日益热门的研究方向。尽管已有显著进展，现有模型仍面临突出挑战，包括需针对不同数据集进行手动超参数调优，以及对强季节性数据中信号与冗余特征的有效区分困难。这些问题制约了时间序列预测模型的泛化能力与实际应用。为解决这些难题，我们提出了一种创新的时间序列预测模型TimeSieve。该方法采用小波变换对时序数据进行预处理，无需额外参数或手动超参数调优即可有效捕获多尺度特征。此外，我们引入信息瓶颈理论，从细节系数与近似系数中滤除冗余特征，仅保留最具预测性的信息。这种组合显著提升了模型的准确性。大量实验表明，我们的模型在70%的数据集上优于现有先进方法，在不同数据集上实现了更高的预测精度与更好的泛化能力。实验结果验证了本方法在解决时间序列预测关键挑战方面的有效性，为实际应用中更可靠高效的预测模型开辟了道路。模型代码发布于https://github.com/xll0328/TimeSieve。