A review on outlier/anomaly detection in time series data

02/11/2020
by   Ane Blázquez-García, et al.
0

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/31/2023

Graph-based Time-Series Anomaly Detection: A Survey

With the recent advances in technology, a wide range of systems continue...
research
05/27/2020

TSML (Time Series Machine Learnng)

Over the past years, the industrial sector has seen many innovations bro...
research
08/21/2023

Adaptive Thresholding Heuristic for KPI Anomaly Detection

A plethora of outlier detectors have been explored in the time series do...
research
04/23/2020

How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series

Recognition of anomalous events is a challenging but critical task in ma...
research
04/07/2022

Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection—Extended Version

Time series data occurs widely, and outlier detection is a fundamental p...
research
12/13/2022

AWT – Clustering Meteorological Time Series Using an Aggregated Wavelet Tree

Both clustering and outlier detection play an important role for meteoro...

Please sign up or login with your details

Forgot password? Click here to reset