Anamoly Detection

Paper #1 - A review on outlier/anomaly detection in time series data

Key Notes

• An observation which deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism

• (Univariate time series) A univariate time series X = {xt }t ∈T is an ordered set of real-valued observations, where each observation is recorded at a specific time
• (Multivariate time series) A multivariate time series X = {xt }t ∈T is defined as an ordered set of kdimensional vectors, each of which is recorded at a specific time

Outliers Type

• Point outliers. A point outlier is a datum that behaves unusually in a specific time instant 
• Subsequence outliers. This term refers to consecutive points in time whose joint behavior is unusual, although each observation individually is not necessarily a point outlier

• multivariate time series is composed of more than one time-dependent variable a univariate analysis can be performed for each variable to detect univariate point outliers

Paper #2 - A Survey on GANs for Anomaly Detection

Notes

• GANs are a framework for the estimation of generative models via an adversarial process in which two models, a discriminator D and a generator G, are trained simultaneously
• The generator G aim is to capture the data distribution, while the discriminator D estimates the probability that a sample came from the training data rather than G

Paper #3 - Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder

Notes

• time series anomaly detection can be in general divided into two settings: 
• i) subsequence or whole sequence level anomaly whereby a subsequence xm,t1:t2 is labeled as an anomaly; 
• ii) point level anomaly for which a measurement xm,t at time t in sequence m is treated as an anomaly. 

Note - Overview of GAN Structure

Notes

• The generator learns to generate plausible data. The generated instances become negative training examples for the discriminator.
• The discriminator learns to distinguish the generator's fake data from real data. The discriminator penalizes the generator for producing implausible results.

More Reads

• Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing
• Autoencoders for unsupervised anomaly detection in high energy physics

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