提出多尺度分解模型，提升季节性时间序列预测精度
A Decomposition Modeling Framework for Seasonal Time-Series Forecasting
- 将时间序列分解为上升、峰值、下降三部分，强化周期特征捕捉
- 在三个公开数据集上，短长期预测误差均降低10%
- 融合卷积与时序卷积，有效建模峰值波动模式
季节性时间序列具有复杂的长程依赖关系，给未来预测带来挑战。本文提出多尺度季节性分解模型（MSSD），首先利用季节性序列的固有周期性，将单变量时间序列分解为上升、峰值和下降三个主要分量，以增强周期特征的捕捉能力。针对现有方法在建模峰值分量上的不足，提出一种多尺度网络结构，能有效捕捉峰值分量中各类潜在波动模式。该模型结合Conv2d与时序卷积网络（TCN），同时捕获全局与局部特征，并引入多尺度重塑机制，进一步增强对峰值波动模式的建模能力。在三个公开可用的季节性数据集上进行验证，结果表明，无论在短期还是长期预测任务中，该方法相比基线模型误差均降低10%。
原文摘要 · Abstract (English)
Seasonal time series exhibit intricate long-term dependencies, posing a significant challenge for accurate future prediction. This paper introduces the Multi-scale Seasonal Decomposition Model (MSSD) for seasonal time-series forecasting. Initially, leveraging the inherent periodicity of seasonal time series, we decompose the univariate time series into three primary components: Ascending, Peak, and Descending. This decomposition approach enhances the capture of periodic features. By addressing the limitations of existing time-series modeling methods, particularly in modeling the Peak component, this research proposes a multi-scale network structure designed to effectively capture various potential peak fluctuation patterns in the Peak component. This study integrates Conv2d and Temporal Convolutional Networks to concurrently capture global and local features. Furthermore, we incorporate multi-scale reshaping to augment the modeling capacity for peak fluctuation patterns. The proposed methodology undergoes validation using three publicly accessible seasonal datasets. Notably, in both short-term and long-term fore-casting tasks, our approach exhibits a 10$\%$ reduction in error compared to the baseline models.
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