Time-Series Anomaly Detection with Implicit Neural Representation

01/28/2022
by   Kyeong-Joong Jeong, et al.
0

Detecting anomalies in multivariate time-series data is essential in many real-world applications. Recently, various deep learning-based approaches have shown considerable improvements in time-series anomaly detection. However, existing methods still have several limitations, such as long training time due to their complex model designs or costly tuning procedures to find optimal hyperparameters (e.g., sliding window length) for a given dataset. In our paper, we propose a novel method called Implicit Neural Representation-based Anomaly Detection (INRAD). Specifically, we train a simple multi-layer perceptron that takes time as input and outputs corresponding values at that time. Then we utilize the representation error as an anomaly score for detecting anomalies. Experiments on five real-world datasets demonstrate that our proposed method outperforms other state-of-the-art methods in performance, training speed, and robustness.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/21/2020

TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks

Anomaly detection in time series data is a significant problem faced in ...
research
02/20/2023

CNTS: Cooperative Network for Time Series

The use of deep learning techniques in detecting anomalies in time serie...
research
01/29/2020

Ensemble Grammar Induction For Detecting Anomalies in Time Series

Time series anomaly detection is an important task, with applications in...
research
10/04/2020

TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series

Multivariate time series (MTS) data are becoming increasingly ubiquitous...
research
06/27/2023

Precursor-of-Anomaly Detection for Irregular Time Series

Anomaly detection is an important field that aims to identify unexpected...
research
12/12/2021

DeepFIB: Self-Imputation for Time Series Anomaly Detection

Time series (TS) anomaly detection (AD) plays an essential role in vario...
research
05/01/2023

Correlation-Driven Multi-Level Multimodal Learning for Anomaly Detection on Multiple Energy Sources

Advanced metering infrastructure (AMI) has been widely used as an intell...

Please sign up or login with your details

Forgot password? Click here to reset