Measuring Anomaly with Algorithmic Entropy
Anomaly detection refers to the identification of observations that are considered outside of normal. Since they are unknown to the system prior to training and rare, the anomaly detection problem is particularly challenging. Model based techniques require large quantities of existing data are to build the model. Statistically based techniques result in the use of statistical metrics or thresholds for determining whether a particular observation is anomalous. I propose a novel approach to anomaly detection using wavelet based algorithmic entropy that does not require modeling or large amounts of data. My method embodies the concept of information distance that rests on the fact that data encodes information. This distance is large when little information is shared, and small when there is greater information sharing. I compare my approach with several techniques in the literature using data obtained from testing of NASA's Space Shuttle Main Engines (SSME)
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