Outlier Detection with RNN Autoencoders

Anomalies, often referred to as outliers, are data points, data sequences or patterns in data which do not conform to the overarching behaviour of the data series. As such, anomaly detection is the task of detecting data points or sequences which don't conform to patterns present in the broader data. The effective detection and removal of anomalous data can provide highly useful insights across a number of business functions, such as detecting broken links embedded within a website, spikes in internet traffic, or dramatic changes in stock prices. Flagging these phenomena as outliers, or enacting a pre-planned response can save businesses both time and money. Anomalous data can typically be separated into three distinct categories, Additive Outliers, Temporal Changes, or Level Shifts. Additive Outliers are characterised by sudden large increases or decreases in value, which can be driven by exogenous or endogenous factors.