A variable-length motifs discovery method in time series using hybrid approach
A variable-length motifs discovery method in time series using hybrid approach
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DOI：
10.1145/3151759.3151781
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Chaw Thet Zan;H. Yamana
中科院分区：
文献类型：
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作者：
Chaw Thet Zan;H. Yamana
从长时间序列中发现重复模式，即所谓的模体，对于向医疗、金融和天气分析等现实世界的应用提供隐藏的知识至关重要。主题可以直接在原始时间序列上发现，也可以在转换后的抽象表示上发现。大多数时间序列模体发现方法需要预先定义模体的长度，这导致执行时间较长，因为要发现不同长度的模体需要改变长度。为了解决这一问题，我们提出了一种将近似方法与精确验证相结合的变长模体发现方法。首先采用符号表示法粗略地发现模体，然后用原始实值数据对发现的模体进行精确检验，以实现快速准确的发现。实验表明，与目前最先进的MK和SBF方法相比，我们提出的方法能够有效地发现有意义的基序。
Discovery of repeated patterns, known as motifs, from long time series is essential for providing hidden knowledge to real-world applications like medical, financial and weather analysis. Motifs can be discovered on raw time series directly or on their transformed abstract representation alternatively. Most of time series motif discovery methods require predefined motif length, which results in long execution time because we have to vary the length to discover motifs with different lengths. To solve the problem, we propose an efficient method for discovering variable length motifs in combination of approximate method with exact verification. First, symbolic representation is adopted to discover motifs roughly followed by exact examination of the found motifs with original real-valued data to achieve fast and exact discovery. The experiments show that our proposed method successfully discovered significant motifs efficiently in comparison with state-of-the-art methods: MK and SBF.