@inproceedings{d0a48f3b8fc64f2b9fbed6c573e5d019,
title = "Research of detection algorithm for time series abnormal subsequence",
abstract = "The recent advancements in sensor technology have made it possible to collect enormous amounts of data in real time. How to find out unusual pattern from time series data plays a very important role in data mining. In this paper, we focus on the abnormal subsequence detection. The original definition of discord subsequences is defective for some kind of time series, in this paper we give a more robust definition which is based on the k nearest neighbors. We also donate a novel method for time series representation, it has better performance than traditional methods (like PAA/SAX) to represent the characteristic of some special time series. To speed up the process of abnormal subsequence detection, we used the clustering method to optimize the outer loop ordering and early abandon subsequence which is impossible to be abnormal. The experiment results validate that the algorithm is correct and has a high efficiency.",
keywords = "Abnormal subsequence, K nearest neighbor, Time series representation",
author = "Chunkai Zhang and Haodong Liu and Ao Yin",
note = "Publisher Copyright: {\textcopyright} 2017, Springer Nature Singapore Pte Ltd.; 3rd International Conference of Pioneer Computer Scientists, Engineers, and Educators, ICPCSEE 2017 ; Conference date: 22-09-2017 Through 24-09-2017",
year = "2017",
doi = "10.1007/978-981-10-6385-5\_2",
language = "英语",
isbn = "9789811063848",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "12--26",
editor = "Xianhua Song and Wei Xie and Zeguang Lu and Beiji Zou and Min Li and Hongzhi Wang",
booktitle = "Data Science - 3rd International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017, Proceedings",
address = "德国",
}