TadGAN: Time Series Anomaly Detection Using GANs (2020)

TadGAN: Time Series Anomaly Detection
Using GANs (2020)
JunPyo Park
Financial Engineering Lab
Department of Industrial Engineering
Alexander Geiger et al.
acXiv

TadGAN
UNIST Financial Engineering Lab. 1

TadGAN - Github
https://github.com/signals-dev/Orion

TadGAN - Github

Abstract
 In this paper, we propose TadGAN, an unsupervised
anomaly detection approach built on Generative
Adversarial Networks (GANs). To capture the temporal
correlations of time series distributions, we use LSTM
Recurrent Neural Networks as base models for
Generators and Critics.
 TadGAN is trained with cycle consistency loss to allow
for effective time-series data reconstruction. We further
propose several novel methods to compute
reconstruction errors, as well as different approaches
to combine reconstruction errors and Critic outputs to
compute anomaly scores.
 Critic ~ Discriminator
(WGAN에서의 Discriminator를 Critic 이라 함)

Time series anomaly detection

Types of anomalies
Collective

Types of anomalies

Traditional(simplest) Methods - Thresholding

Statistical Methods - ARIMA

Deep learning-based methods

Performance
Important Question: Do these new, complex
approaches perform better than a simple baseline
statistical method?

Unsupervised Time Series Anomaly Detection
M개
T기간
Problem Setting
Difficulties
- Do not have previously identified “known anomalies”
- Non availability of “normal baselines”
- Not all detected anomalies are problematic (e.g., Regime change)
- The length of ai is also variable and is not known a priori.
- Evaluation Problem: How to evaluate?

Methodology: Proximity-based methods (KNN, LOF)

Methodology: Prediction-based methods (ARIMA, LSTM, RNNs…)

Methodology: Reconstruction-based methods (AE, VAE, GANs…)

DCGAN(Simplest)

WGAN

TAnoGAN

MADGAN

TadGAN – What is difference?
- GAN-reconstruction based anomaly detection method for time series data.
- Introduce a cycle-consistent GAN architecture for time-series-to-time-series
mapping
- Identify two time series similarity measures suitable for evaluating the contextual
similarity between original and GAN-reconstructed sequences.
- Conduct extensive evaluation
- Develop a benchmarking system
WGAN, BiGAN, CycleGAN, Timeseries GAN 등을 참고하여
개선

BiGAN

CycleGAN

Cycle-consistency Loss
Wasserstein loss
Full Objective

Input: Sliding Window
Training
Reconstruction

Reconstruct the whole time series
Calculate Reconstruction Scores: Simple Method

Calculate Reconstruction Scores: DTW
DTW: Dynamic Time Warping

Calculate Critic Scores: Normalized

Total score

Thank you for listening!

TadGAN: Time Series Anomaly Detection Using GANs (2020)

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