分离趋势与季节成分，提升多变量时间序列预测精度。
Revisiting the Seasonal Trend Decomposition for Enhanced Time Series Forecasting
- 分别处理趋势与季节成分，优化建模策略。
- 在4个基准模型上平均降低10% MSE误差。
- 适用于水文等真实场景，保持线性计算复杂度。
时间序列预测在多个领域面临挑战。本文基于时序分解，改进机器学习模型以增强多变量时间序列预测能力。针对趋势与季节成分分别设计方法：趋势成分采用可逆实例归一化，季节成分则直接使用主干模型，不进行归一化或缩放。该策略有效降低了现有先进模型的误差，并提出双MLP模型作为更高效的解决方案。在四个主流基准数据集上，平均降低10% MSE；在来自美国地质调查局（USGS）的水文数据集上也取得显著提升，同时保持线性时间复杂度，验证了其实际应用价值。源代码已公开于https://github.com/Sanjeev97/Time-Series-Decomposition。
原文摘要 · Abstract (English)
Time series forecasting presents significant challenges in real-world applications across various domains. Building upon the decomposition of the time series, we enhance the architecture of machine learning models for better multivariate time series forecasting. To achieve this, we focus on the trend and seasonal components individually and investigate solutions to predict them with less errors. Recognizing that reversible instance normalization is effective only for the trend component, we take a different approach with the seasonal component by directly applying backbone models without any normalization or scaling procedures. Through these strategies, we successfully reduce error values of the existing state-of-the-art models and finally introduce dual-MLP models as more computationally efficient solutions. Furthermore, our approach consistently yields positive results with around 10% MSE average reduction across four state-of-the-art baselines on the benchmark datasets. We also evaluate our approach on a hydrological dataset extracted from the United States Geological Survey (USGS) river stations, where our models achieve significant improvements while maintaining linear time complexity, demonstrating real-world effectiveness. The source code is available at https://github.com/Sanjeev97/Time-Series-Decomposition
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