ESPSA: A prediction-based algorithm for streaming time series segmentation

• Published in: Expert systems with applications Vol. 41; no. 14; pp. 6098 - 6105
• Main Authors: Li, Guiling, Cai, Zhihua, Kang, Xiaojun, Wu, Zongda, Wang, Yuanzhen
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 15.10.2014; Elsevier
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Computational efficiency; Computer science; control theory; systems; Computing time; Data processing. List processing. Character string processing; Error analysis; Exact sciences and technology; Exponential smoothing; Inference from stochastic processes; time series analysis; Mathematical models; Mathematics; Memory organisation. Data processing; Probability and statistics; Sciences and techniques of general use; Segmentation; Sliding window; Smoothing; Software; Statistics; Streaming time series; Theoretical computing; Time series; Exponential smoothing; Streaming time series; Segmentation; Sliding window; Streaming; Smoothing methods; Statistical analysis; Error estimation; Time series; Algorithmics; Cluster; Data mining; Data smoothing; Modeling; Data flow; Compression ratio; Efficiency; Classification; NP hard problem; Exponential time; Indexing
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2014.03.043

•We propose exponential smoothing prediction-based segmentation algorithm.•The single exponential smoothing method is used to predict future data elements.•The prediction error criterion is proposed to determine segmenting key points.•The experiments demonstrate the effectiveness and efficiency of the proposed algorithm. Streaming time series segmentation is one of the major problems in streaming time series mining, which can create the high-level representation of streaming time series, and thus can provide important supports for many time series mining tasks, such as indexing, clustering, classification, and discord discovery. However, the data elements in streaming time series, which usually arrive online, are fast-changing and unbounded in size, consequently, leading to a higher requirement for the computing efficiency of time series segmentation. Thus, it is a challenging task how to segment streaming time series accurately under the constraint of computing efficiency. In this paper, we propose exponential smoothing prediction-based segmentation algorithm (ESPSA). The proposed algorithm is developed based on a sliding window model, and uses the typical exponential smoothing method to calculate the smoothing value of arrived data element of streaming time series as the prediction value of the future data. Besides, to determine whether a data element is a segmenting key point, we study the statistical characteristics of the prediction error and then deduce the relationship between the prediction error and the compression rate. The extensive experiments on both synthetic and real datasets demonstrate that the proposed algorithm can segment streaming time series effectively and efficiently. More importantly, compared with candidate algorithms, the proposed algorithm can reduce the computing time by orders of magnitude.