A Fast Similarity Query Method Based on Inter-Relevant Successive Trees Model in Time Series

Time series are an important type of data. Similarity querying in time series is a basic task to analyze the changing trend of time series. In this paper, a novel method is proposed, which supports fast search similar pattern in time series. It first segments time series based on a series of perceptually important points, and then time series are converted into meaningful symbol sequences in terms of the segment’s features and MATH categorization. After that, a new index model is designed, which is called inter-relevant successive trees（IRST）, to achieve fast similarity retrieval in multiple time series. Compared with the previous methods, the method is more efficient and allows different lengths matching.