Interval-Valued Time Series Classification Using $D_K$-Distance

• URL: http://arxiv.org/abs/2504.04667v1
• Date: Mon, 07 Apr 2025 01:31:31 GMT
• Title: Interval-Valued Time Series Classification Using $D_K$-Distance
• Authors: Wan Tian, Zhongfeng Qin,
• Abstract summary: We introduce a classification approach that treats intervals as unified entities.<n>In theory, we derived a sharper excess risk bound for deep multiclassifiers based on offset Rademacher complexity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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