# Anomaly detection and motif discovery in symbolic representations of time series

## TL;DR

This paper presents algorithms for anomaly detection and motif discovery in symbolic time series data using SAX, along with a benchmark based on cloud monitoring data to evaluate their effectiveness.

## Contribution

It introduces new algorithms for anomaly detection and motif discovery in symbolic time series, and provides a benchmark for evaluating these methods on cloud monitoring data.

## Key findings

- Algorithms effectively detect anomalies in symbolic time series.
- Benchmark results show competitive performance of proposed methods.
- The approach is scalable to large industrial datasets.

## Abstract

The advent of the Big Data hype and the consistent recollection of event logs and real-time data from sensors, monitoring software and machine configuration has generated a huge amount of time-varying data in about every sector of the industry. Rule-based processing of such data has ceased to be relevant in many scenarios where anomaly detection and pattern mining have to be entirely accomplished by the machine. Since the early 2000s, the de-facto standard for representing time series has been the Symbolic Aggregate approXimation (SAX).In this document, we present a few algorithms using this representation for anomaly detection and motif discovery, also known as pattern mining, in such data. We propose a benchmark of anomaly detection algorithms using data from Cloud monitoring software.