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Second-order Confidence Network for Early Classification of Time Series

Published: 19 December 2023 Publication History

Abstract

Time series data are ubiquitous in a variety of disciplines. Early classification of time series, which aims to predict the class label of a time series as early and accurately as possible, is a significant but challenging task in many time-sensitive applications. Existing approaches mainly utilize heuristic stopping rules to capture stopping signals from the prediction results of time series classifiers. However, heuristic stopping rules can only capture obvious stopping signals, which makes these approaches give either correct but late predictions or early but incorrect predictions. To tackle the problem, we propose a novel second-order confidence network for early classification of time series, which can automatically learn to capture implicit stopping signals in early time series in a unified framework. The proposed model leverages deep neural models to capture temporal patterns and outputs second-order confidence to reflect the implicit stopping signals. Specifically, our model exploits the data not only from a time step but also from the probability sequence to capture stopping signals. By combining stopping signals from the classifier output and the second-order confidence, we design a more robust trigger to decide whether or not to request more observations from future time steps. Experimental results show that our approach can achieve superior results in early classification compared to state-of-the-art approaches.

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  1. Second-order Confidence Network for Early Classification of Time Series

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      Published In

      cover image ACM Transactions on Intelligent Systems and Technology
      ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 1
      February 2024
      533 pages
      EISSN:2157-6912
      DOI:10.1145/3613503
      • Editor:
      • Huan Liu
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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 19 December 2023
      Online AM: 02 November 2023
      Accepted: 19 October 2023
      Revised: 09 May 2023
      Received: 29 August 2022
      Published in TIST Volume 15, Issue 1

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      1. Early classification
      2. time series classification
      3. confidence network

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      • Natural Science Foundation of China
      • Program for Chang Jiang Scholars and Innovative Research Team in University (PCSIRT) of the Ministry of Education
      • Provincial Postgraduate Innovation and Entrepreneurship Project

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