季节性时间序列预测的分解建模框架
A Decomposition Modeling Framework for Seasonal Time-Series Forecasting
AI总结：
本文提出多尺度季节性分解模型（MSSD），通过分解时间序列为上升、峰值和下降成分，并整合Conv2d与TCN及多尺度重塑，在季节性预测中较基线降低10%误差。
摘要：
季节性时间序列展现出复杂的长程依赖性，这对准确预测未来构成了重大挑战。本文介绍了用于季节性时间序列预测的多尺度季节性分解模型（MSSD）。首先，利用季节性时间序列固有的周期性，我们将单变量时间序列分解为三个主要组成部分：上升段、峰值段和下降段。这种分解方法增强了对周期性特征的捕获。针对现有时间序列建模方法在建模峰值段方面的局限性，本研究提出了一种多尺度网络结构，旨在有效捕获峰值段中各种潜在的峰值波动模式。本研究整合了Conv2d和时序卷积网络，以同时捕获全局和局部特征。此外，我们引入了多尺度重塑来增强对峰值波动模式的建模能力。所提出的方法使用三个公开可用的季节性数据集进行了验证。值得注意的是，在短期和长期预测任务中，我们的方法相比基线模型实现了10%的误差降低。
英文摘要：
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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