DAMP: accurate time series anomaly detection on trillions of datapoints and ultra-fast arriving data streams

Time series anomaly detection is one of the most active areas of research in data mining, with dozens of new approaches been suggested each year. In spite of all these creative solutions proposed for this problem, recent empirical evidence suggests that the time series discord, a relatively simple twenty-year old distance-based technique, remains among the state-of-art techniques. While there are many algorithms for computing the time series discords, they all have limitations. First, they are limited to the batch case, whereas the online case is more actionable. Second, these algorithms exhibit poor scalability beyond tens of thousands of datapoints. In this work we introduce DAMP, a novel algorithm that addresses both these issues. DAMP computes exact left-discords on fast arriving streams, at up to 300,000 Hz using a commodity desktop. This allows us to find time series discords in datasets with trillions of datapoints for the first time. We will demonstrate the utility of our algorithm with the most ambitious set of time series anomaly detection experiments ever conducted. We will further show that our speedup improvements can be applied in the multidimensional case.