In this post, we discuss various Deep Anomaly Detection Models (DAD) classified based on the availability of labels and training objectives. For each model types domain, we discuss the following four aspects: * Assumptions * Type or model architectures * Computational complexity * Advantage and disadvantages 1. Supervised Deep Anomaly Detection Supervised anomaly detection
Anomaly detection is applicable in a very large number and variety of domains, such as fraud detection, fault detection, system health monitoring, event detection in sensor networks, detecting ecosystem disturbances, defect detection in images using machine vision, medical diagnosis and law enforcement. In this post, we will discuss several applications
Varous deep learning mothods are used to detect anomaly. When we encounter the difficulty to choose an apropriate one, we can always find some cludes from the nature of input data, the availability of labels, the training objective, the type of anomaly, and the output of DAD techniques. 1. Nature
In data analysis, anomaly detection (also referred to as outlier detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data. Anomalies can be caused by errors in the data but sometimes are indicative of a new, previously
According to the CAPM model, the relationship between premium return of individual share and that of S&P 500 should be approximately linear. \[r_{i} - r_{f} = \beta_i (r_{m} - r_{f})+\epsilon_{i}\] However, when we contemplate the two variables, it is not difficult to
Covariance matrix estimation is fundamental for almost all areas of multivariate analysis and many other applied problems. For instance, according to Markowitz's portfolio theory, the eigenvectors of the return covariance matrix determine the portfolio allocation. Intuitive thought is to take the sample covariance matrix as the estimation of
Short Answer * In time series analysis, dynamic time warping (DTW) is one of the algorithms for measuring similarity between two temporal sequences, which may vary in speed. * DTW has been applied to temporal sequences of video, audio, and graphics data. Any data that can be turned into a linear sequence
* According to a 2020 report, over a 15-year period, nearly 90% of actively managed investment funds failed to beat the market. This may not surprise you. But back in 1976, when Bogle created the First Index Investment Trust as the first index mutual fund available to the general public, passive
* Multivariate testing (MVT) techniques apply statistical hypothesis testing on multi-variable systems. Different from A/B testing which changes one variation at one time, MVT is a form of testing in which changes are made to multiple sections of a webpage on a site, and then variations are created for all
Short Answer * Isolation Forest (iForest) is a machine learning algorithm for anomaly detection. Instances, which have an average shorter path length in the trained isolation forest, are classified as anomalous points. Long Answer * The isolation Forest algorithm is a very effective and intuitive anomaly detection method, which was first proposed
Short Answer * A Gaussian Mixture Model (GMM) is a composite distribution made by \(K\) Gaussian sub-distributions each with its own probability distribution function represented by \(N(\mathbf{\mu}_ {i}, \mathbf{\Sigma}_ {i})\) with means \(\mathbf{\mu}_ {i}\) and variance \(\mathbf{\Sigma}_ {i}\). The mixture component weights are defined as \(\phi_
Short Answer * Expectation Maximization (EM) is an iterative algorithm for finding the maximum a posteriori (MAP) estimated of parameters in statistical modles, where the model depends on unobserved latent variables. * The algorithm iteratively repeats two steps until convergence or until a maximum number of iteratively repeats two steps until convergence