@inproceedings{aa0a8f58b9e94e9088e00712f6078c46,
title = "A novel symbolic aggregate approximation for time series",
abstract = "Symbolic Aggregate approximation (SAX) is a classical symbolic approach in many time series data mining applications. However, SAX only reflects the segment mean value feature and misses important information in a segment, namely the trend of the value change in the segment. Such a miss may cause a wrong classification in some cases, since the SAX representation cannot distinguish different time series with similar average values but different trends. In this paper, we present Trend Feature Symbolic Aggregate approximation (TFSAX) to solve this problem. First, we utilize Piecewise Aggregate Approximation (PAA) approach to reduce dimensionality and discretize the mean value of each segment by SAX. Second, extract trend feature in each segment by using trend distance factor and trend shape factor. Then, design multi-resolution symbolic mapping rules to discretize trend information into symbols. We also propose a modified distance measure by integrating the SAX distance with a weighted trend distance. We show that our distance measure has a tighter lower bound to the Euclidean distance than that of the original SAX. The experimental results on diverse time series data sets demonstrate that our proposed representation significantly outperforms the original SAX representation and an improved SAX representation for classification.",
keywords = "Distance measure, Lower bound, Symbolic aggregate approximation, Time series, Trend feature",
author = "Yufeng Yu and Yuelong Zhu and Dingsheng Wan and Huan Liu and Qun Zhao",
note = "Funding Information: Acknowledgements. This work has been partially supported by the National Key Research and Development Program of China (No. 2018YFC1508100), the Fundamental Research Funds for the Central Universities (No. 2018B45614) and the CSC Scholarship. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019 ; Conference date: 04-01-2019 Through 06-01-2019",
year = "2019",
doi = "10.1007/978-3-030-19063-7_65",
language = "English (US)",
isbn = "9783030190620",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "805--822",
editor = "Roslan Ismail and Hyunseung Choo and Sukhan Lee",
booktitle = "Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication, IMCOM 2019",
}