High Dimensional Time Series Anomaly Detection
Abstract
Anomaly Detection task is to determine critical data points whose behaviour deviates unexpectedly from usual data points behaviour. These anomalous data points might indicate a major
fault in a manufacturing unit, security glitch on a server, fraud in banking system or abnormal
functioning of a human body part. These data are usually recorded using several high precision
sensors to capture multiple contributing factors as multivariate time series data.
To determine the anomalous data on such high dimensional real world data using data driven
machine learning approaches, it is important to extract temporal information among data points
and latent feature based methods are instinctive choice. In this thesis primarily, we will propose
an Anomaly Aware Matrix Factorization (ATMF) method with two temporal neighborhood term ,
the autoregressive bias which could learn local patterns and the moving average bias, which could
smoothen noises. To develop an optimization function which will be robust to outliers we will use
an approx mean absolute error function. ATMF performances will be demonstrated using 5 real
world dataset. This thesis further propose future modifications in this model to encode contextual
information within model using a minimum arborescence tree.
In the second part, we will briefly discuss, an automated time series outlier detection System (TODS) package for high dimensional time series data, which has several modules for data
preprocessing, feature extractions and anomaly detection
Citation
Kumar, Devesh (2021). High Dimensional Time Series Anomaly Detection. Master's thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /195258.