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Implementation of
MSᴛʀᴇᴀᴍ detects group anomalies from a multi-aspect data stream in constant time and memory. We output an anomaly score for each record. MSᴛʀᴇᴀᴍ builds on top of MIDAS to work in a multi-aspect setting such as event-log data, multi-attributed graphs etc.
MSᴛʀᴇᴀᴍ expects the input multi-aspect record stream to be stored in three files:
Both Numerical and Categorical files contain corresponding features of the multi-aspect record. Records should be sorted in non-decreasing order of their time stamps and the column delimiter should be ,
If you use this code for your research, please consider citing our WWW paper.
@inproceedings{bhatia2021mstream,
title={Fast Anomaly Detection in Multi-Aspect Streams},
author={Siddharth Bhatia and Arjit Jain and Pan Li and Ritesh Kumar and Bryan Hooi},
booktitle={The Web Conference (WWW)},
year={2021}
}
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