用高斯过程生成数据，测试模型对时序特征的适应能力。
Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated Data
- 用高斯过程构建可控数据集，精确控制时序特征
- 提出TimeFlex模型，模块化设计适配不同动态模式
- 首次系统验证模型性能与数据特征的匹配关系
深度学习的发展显著提升了时间序列预测能力，使其能更准确地建模序列数据中的复杂时序依赖。然而，现有方法多在有限的真实数据集上进行比较，难以揭示特定数据特征与模型架构优势之间的关联。本文旨在建立时序特征与模型性能之间的明确联系。为此，我们提出一种基于高斯过程生成的新数据集，可精准呈现已知的时序特性，用于针对性评估模型对不同特征的适应能力。同时，我们提出TimeFlex模型，采用模块化架构，可有效处理趋势、周期性等多样化时序动态。该模型与当前主流方法对比，揭示了不同模型在各类时序条件下的表现差异，为模型选型提供依据。
原文摘要 · Abstract (English)
Developments in Deep Learning have significantly improved time series forecasting by enabling more accurate modeling of complex temporal dependencies inherent in sequential data. The effectiveness of such models is often demonstrated on limited sets of specific real-world data. Although this allows for comparative analysis, it still does not demonstrate how specific data characteristics align with the architectural strengths of individual models. Our research aims at uncovering clear connections between time series characteristics and particular models. We introduce a novel dataset generated using Gaussian Processes, specifically designed to display distinct, known characteristics for targeted evaluations of model adaptability to them. Furthermore, we present TimeFlex, a new model that incorporates a modular architecture tailored to handle diverse temporal dynamics, including trends and periodic patterns. This model is compared to current state-of-the-art models, offering a deeper understanding of how models perform under varied time series conditions.
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