Fast And Accurate Time Series Classification Through Supervised Interval Search
20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2020)(2020)
摘要
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 series representations prior to training any tree classifier, where the relevance of a sub-series is measured by feature ranking metrics (i.e., supervision signals). Experiments on extensive real datasets show that STSF achieves comparable accuracy to state-of-the-art TSC methods while being significantly more efficient, enabling TSC for long time series.
更多查看译文
关键词
Time series classification, Interval-based classifier, Feature selection
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要