In the research of control chart pattern recognition, most previous methods used a classifier to label abnormal control chart patterns. However, long-term control chart data often contains a large number of small abnormal patterns, with characteristics unlike those seen from a global view of the entire chart. There is also a high probability that local abnormal patterns are worthy of analysis. This study presents a novel multi-scale control chart pattern recognition scheme, MS-CCPR, which does not focus on the classification of data from a single chart. Rather, the proposed scheme uses a proposed histogram-based data representation in conjunction with time series subsequence matching to identify abnormal patterns on various scales from a long series of control charts. Experimental results demonstrate the efficacy of the proposed framework in the efficient detection of chart patterns at various scales, outperforming the state-of-the-art time series subsequence matching algorithms.
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