用距离度量将区间时间序列转为图像，实现高效分类。
Interval-Valued Time Series Classification Using $D_K$-Distance
- 基于DK距离扩展点序列成像法，处理区间时间序列
- 在仿真与真实数据中均优于传统点序列分类方法
- 适合金融、经济等含区间数据的多类分类任务
近年来，区间值时间序列在计量经济学、金融和统计学领域受到越来越多关注。然而，现有研究主要集中在单变量和多变量区间值时间序列的预测与统计推断，忽视了分类这一重要方向。本文提出一种将区间视为整体的分类方法，适用于单变量和多变量区间值时间序列。首先，利用DK距离将点值时间序列成像方法扩展至区间值场景，实现区间值时间序列的成像；随后，在生成的图像数据集上使用合适的深度学习模型进行分类，以实现区间值时间序列的分类目标。理论上，我们基于偏置Rademacher复杂度，推导出更紧的深度多分类器过失风险界。最后，通过与多种现有点值时间序列分类方法在模拟研究和真实数据分析中的对比，验证了所提方法的优越性。
原文摘要 · Abstract (English)
In recent years, modeling and analysis of interval-valued time series have garnered increasing attention in econometrics, finance, and statistics. However, these studies have predominantly focused on statistical inference in the forecasting of univariate and multivariate interval-valued time series, overlooking another important aspect: classification. In this paper, we introduce a classification approach that treats intervals as unified entities, applicable to both univariate and multivariate interval-valued time series. Specifically, we first extend the point-valued time series imaging methods to interval-valued scenarios using the $D_K$-distance, enabling the imaging of interval-valued time series. Then, we employ suitable deep learning model for classification on the obtained imaging dataset, aiming to achieve classification for interval-valued time series. In theory, we derived a sharper excess risk bound for deep multiclassifiers based on offset Rademacher complexity. Finally, we validate the superiority of the proposed method through comparisons with various existing point-valued time series classification methods in both simulation studies and real data applications.
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。