# Fast, parameter-free time series anomaly detection
## Getting started
## The STAN Algorithm
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STAN (**s**ummary s**ta**tistics e**n**semble) is a parameter-free time series anomaly detection algorithm. It is designed to be simple and fast at detecting the most anomalous region within a univariate time series.
The idea behind STAN is to incorporate the sliding window technique over all nonoverlapping time series subsequences with predefined length based on the periodical length of the time series. STAN applies a set of summary statistics over the time series subsequences. For our experiments we used a set of 8 summary statistics (e.g. Min, Max, Mean, ...), but feel free to experiment with other aggregate functions if you would like. Based on the results of the summary statistics STAN chooses the potentially most anomalous region within the time series. At the end, STAN returns the index of this anomalous subsequence.
The source code can be found in the file STAN.py. Running this script will execute STAN of the UCR Anomaly Archive and will return the anomaly detection accuracy (UCR-Score), the execution time, and the individual anomaly detection acuracies of each summary statistic.
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## Add your files
## UCR Anomaly Archive
The folder UCR Anomaly Archive contains 250 time series, which we used to evaluate STAN.
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@@ -53,7 +59,7 @@ When you're ready to make this README your own, just edit this file and use the
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Fast, parameter-free time series anomaly detection
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