Abstract
Anomaly detection is a subfield of machine learning where a model is produced which pays special attention to any variations from the norm in the data. Anomaly detection has a wide variety of uses in different domains such as fraud detection, intrusion detection, event detection in sensor networks, etc. This paper will study anomaly detection in time series and some techniques such as one-class SVM and isolation forest which are used to identify the anomalous data points in the time-series. We present our comparative study on unsupervised machine learning algorithms which are effective for monitoring machines in hydropower plants using the data which are generated from the sensors.
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Rai, A. (2021). Unsupervised Learning Algorithms for Hydropower’s Sensor Data. In: Gunjan, V.K., Suganthan, P.N., Haase, J., Kumar, A. (eds) Cybernetics, Cognition and Machine Learning Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6691-6_11
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DOI: https://doi.org/10.1007/978-981-33-6691-6_11
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