Abstract
In time series classification, one of the most popular models is Bag-Of-Patterns (BOP). Most BOP methods run in super-linear time. A recent work proposed a linear time BOP model, yet it has limited accuracy. In this work, we present Hybrid Bag-Of-Patterns (HBOP), which can greatly enhance accuracy while maintaining linear complexity. Concretely, we first propose a novel time series discretization method called SLA, which can retain more information than the classic SAX. We use a hybrid of SLA and SAX to expressively and compactly represent subsequences, which is our most important design feature. Moreover, we develop an efficient time series transformation method that is key to achieving linear complexity. We also propose a novel X-means clustering subroutine to handle subclasses. Extensive experiments on over 100 datasets demonstrate the effectiveness and efficiency of our method.
This work is funded by NSFC Grant 61672161 and Dongguan Innovative Research Team Program 2018607201008. We sincerely thank Dr Hoang Anh Dau from University of California, Riverside for responding to our inquiries on the UCR Archive [5].
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Notes
- 1.
Here we have ignored the Fungi dataset on which we have obtained abnormally short classification time for BOPF. See [1] for more on this.
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Liang, S., Zhang, Y., Ma, J. (2020). Enhancing Linear Time Complexity Time Series Classification with Hybrid Bag-Of-Patterns. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12112. Springer, Cham. https://doi.org/10.1007/978-3-030-59410-7_50
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