TadGAN is an unsupervised time series anomaly detection method that uses generative adversarial networks. It introduces a cycle-consistent GAN architecture to map time series data to reconstructed time series. TadGAN trains the GAN with cycle consistency loss and Wasserstein loss. It evaluates anomalies based on reconstruction error scores calculated using simple and dynamic time warping methods, as well as normalized critic scores. The paper aims to improve upon other time series GAN methods like WGAN, BiGAN, and CycleGAN through this approach.

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

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

4. TadGAN - Github
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5. 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)
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6. Time series anomaly detection
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7. Types of anomalies
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Collective

8. Types of anomalies
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9. Types of anomalies
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10. Traditional(simplest) Methods - Thresholding
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11. Statistical Methods - ARIMA
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12. Deep learning-based methods
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13. Performance
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Important Question: Do these new, complex
approaches perform better than a simple baseline
statistical method?

14. Unsupervised Time Series Anomaly Detection
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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?

15. Unsupervised Time Series Anomaly Detection
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Methodology: Proximity-based methods (KNN, LOF)

16. Unsupervised Time Series Anomaly Detection
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Methodology: Prediction-based methods (ARIMA, LSTM, RNNs…)

17. Unsupervised Time Series Anomaly Detection
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Methodology: Reconstruction-based methods (AE, VAE, GANs…)

18. DCGAN(Simplest)
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19. WGAN
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20. WGAN
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21. TAnoGAN
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22. TAnoGAN
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23. TAnoGAN
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24. TAnoGAN
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25. TAnoGAN
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26. MADGAN
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27. MADGAN
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28. TadGAN – What is difference?
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- 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

29. BiGAN
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30. CycleGAN
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31. CycleGAN
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32. CycleGAN
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33. TadGAN – What is difference?
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34. TadGAN – What is difference?
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Cycle-consistency Loss
Wasserstein loss
Full Objective

35. TadGAN – What is difference?
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Input: Sliding Window
Training
Reconstruction

36. TadGAN – What is difference?
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Reconstruct the whole time series
Calculate Reconstruction Scores: Simple Method

37. TadGAN – What is difference?
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Calculate Reconstruction Scores: DTW
DTW: Dynamic Time Warping

38. TadGAN – What is difference?
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Calculate Critic Scores: Normalized

39. TadGAN – What is difference?
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Total score

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Thank you for listening!