International audienceAnomaly detection in time series has attracted considerable attention due to its importance in many real-world applications including intrusion detection, energy management and finance. Most approaches for detecting outliers rely on either manually set thresholds or assumptions on the distribution of data according to Chandola, Banerjee and Kumar. Here, we propose a new approach to detect outliers in streaming univariate time series based on Extreme Value Theory that does not require to hand-set thresholds and makes no assumption on the distribution: the main parameter is only the risk, controlling the number of false posi-tives. Our approach can be used for outlier detection, but more generally for automatically setting thresholds, making it useful in wide number of situations. We also experiment our algorithms on various real-world datasets which confirm its soundness and efficiency
In this paper we present a novel algorithm for discovering tree patterns in a tree database. This algorithm uses a relaxed tree inclusion definition, making the problem more complex (checking tree inclusion is NP-complete), but allowing to mine highly heterogeneous databases. To obtain good performances, our DRYADE algorithm discovers only closed frequent tree patterns.
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