@inproceedings{d26ff2f2d1d842d5900fc57773db4cce,
title = "Multi-resolution time series discord discovery",
abstract = "Discord Discovery is a recent approach for anomaly detection in time series that has attracted much research because of the wide variety of real-world applications in monitoring systems. However, finding anomalies by different levels of resolution has received little attention in this research line. In this paper, we introduce a multi-resolution representation based on local trends and mean values of the time series. We require the level of resolution as parameter, but it can be automatically computed if we consider the maximum resolution of the time series. In order to provide a useful representation for discord discovery, we propose dissimilarity measures for achieving high effective results, and a symbolic representation based on SAX technique for efficient searches using a multi-resolution indexing scheme. We evaluate our method over a diversity of data domains achieving a better performance compared with some of the best-known classic techniques.",
keywords = "Anomaly detection, Discord discovery, Indexing, Time series",
author = "Heider Sanchez and Benjamin Bustos",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017 ; Conference date: 11-06-2017 Through 15-06-2017",
year = "2017",
doi = "10.1007/978-3-319-59147-6\_11",
language = "English",
isbn = "9783319591469",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "116--128",
editor = "Ignacio Rojas and Andreu Catala and Gonzalo Joya",
booktitle = "Advances in Computational Intelligence - 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, Proceedings",
}