用分解损失提升时间序列预测，尤其改善趋势与季节性建模。
DBLoss: Decomposition-based Loss Function for Time Series Forecasting
- 将预测时序分解为趋势与季节成分，分别计算损失
- 在多个真实数据集上显著提升主流模型性能
- 可适配任意深度学习模型，设计思路新颖
时间序列预测在经济、交通、能源和AIOps等领域具有重要价值，精准预测有助于科学决策。然而，现有的均方误差（MSE）损失函数即使在前向传播中使用分解模块分离趋势与季节性，仍难以准确捕捉预测期内的趋势或季节模式。为此，我们提出一种简单而有效的基于分解的损失函数——DBLoss。该方法利用指数移动平均在预测时域内将时间序列分解为季节性和趋势成分，分别计算各成分的损失并加权组合。作为通用损失函数，DBLoss可与任意深度学习预测模型结合。大量实验证明，它显著提升了多种主流模型在不同真实数据集上的表现，并为时间序列损失函数的设计提供了新视角。
原文摘要 · Abstract (English)
Time series forecasting holds significant value in various domains such as economics, traffic, energy, and AIOps, as accurate predictions facilitate informed decision-making. However, the existing Mean Squared Error (MSE) loss function sometimes fails to accurately capture the seasonality or trend within the forecasting horizon, even when decomposition modules are used in the forward propagation to model the trend and seasonality separately. To address these challenges, we propose a simple yet effective Decomposition-Based Loss function called DBLoss. This method uses exponential moving averages to decompose the time series into seasonal and trend components within the forecasting horizon, and then calculates the loss for each of these components separately, followed by weighting them. As a general loss function, DBLoss can be combined with any deep learning forecasting model. Extensive experiments demonstrate that DBLoss significantly improves the performance of state-of-the-art models across diverse real-world datasets and provides a new perspective on the design of time series loss functions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。