Unsupervised Anomaly Detection

Abstract

This research focuses on Unsupervised Anomaly Detection using the "ambient_temperature_system_failure.csv" dataset from Numenta Anomaly Benchmark (NAB). The dataset contains time-series temperature readings from an industrial machine's sensor. The aim is to detect anomalies indicating system failures or aberrant behavior without labeled data. Various algorithms, such as K-means, Gaussian/Elliptic Envelopes, Markov Chain, Isolation Forest, One-Class SVM, and RNNs, are applied to analyze the temperature data. These algorithms are chosen for their ability to identify significant deviations in unlabeled datasets. The study explores how these techniques enhance anomaly understanding in time series data, relevant in manufacturing, healthcare, and finance. This research's novelty lies in employing unsupervised learning techniques on a real-world dataset and understanding theiradaptability in anomaly detection. The results are expected to contribute valuable insights to the field, showcasing the practicality and effectiveness of these algorithms across various scenarios.