How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?

02/12/2021
by   Ming-Chang Lee, et al.
0

Anomaly detection is the process of identifying unexpected events or ab-normalities in data, and it has been applied in many different areas such as system monitoring, fraud detection, healthcare, intrusion detection, etc. Providing real-time, lightweight, and proactive anomaly detection for time series with neither human intervention nor domain knowledge could be highly valuable since it reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous event occurs. To our knowledge, RePAD (Real-time Proactive Anomaly Detection algorithm) is a generic approach with all above-mentioned features. To achieve real-time and lightweight detection, RePAD utilizes Long Short-Term Memory (LSTM) to detect whether or not each upcoming data point is anomalous based on short-term historical data points. However, it is unclear that how different amounts of historical data points affect the performance of RePAD. Therefore, in this paper, we investigate the impact of different amounts of historical data on RePAD by introducing a set of performance metrics that cover novel detection accuracy measures, time efficiency, readiness, and resource consumption, etc. Empirical experiments based on real-world time series datasets are conducted to evaluate RePAD in different scenarios, and the experimental results are presented and discussed.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/05/2020

ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series

Anomaly detection is an active research topic in many different fields s...
research
01/24/2020

RePAD: Real-time Proactive Anomaly Detection for Time Series

During the past decade, many anomaly detection approaches have been intr...
research
04/19/2021

SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series

Real-world time series data often present recurrent or repetitive patter...
research
03/17/2023

A Bi-LSTM Autoencoder Framework for Anomaly Detection – A Case Study of a Wind Power Dataset

Anomalies refer to data points or events that deviate from normal and ho...
research
04/12/2023

NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series

As more connected devices are implemented in a cyber-physical world and ...
research
07/18/2022

RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers

CyberPhysical systems (CPS) must be closely monitored to identify and po...
research
03/08/2020

Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection

The amount of data in real-time, such as time series and streaming data,...

Please sign up or login with your details

Forgot password? Click here to reset