Conference Proceedings

Fast and Accurate Time Series Classification Through Supervised Interval Search

Nestor Cabello, Elham Naghizade, Jianzhong Qi, Lars Kulik, C Plant (ed.), H Wang (ed.), A Cuzzocrea (ed.), C Zaniolo (ed.), X Wu (ed.)

20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2020) | IEEE COMPUTER SOC | Published : 2020

Abstract

Time series classification (TSC) aims to predict the class label of a given time series. Modern applications such as appliance modelling require to model an abundance of long time series, which makes it difficult to use many state-of-the-art TSC techniques due to their high computational cost and lack of interpretable outputs. To address these challenges, we propose a novel TSC method: the Supervised Time Series Forest (STSF). STSF improves the classification efficiency by examining only a (set of) sub-series of the original time series, and its tree-based structure allows for interpretable outcomes. STSF adapts a top-down approach to search for relevant sub-series in three different time se..

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