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Summary
- Point Anomalies - Value is far outside the entirety of the data set
- Conditional Outliers - With respect to context, Same value may not be anamoly in another time
- Collective Outliers - Set of 1 or more points that deviate from dataset
Key Notes
- Clustering methods do not require the data to be labeled, making it a good fit for our unsupervised task. Very sensitive to outlier data points
Two-Step Process
- The number of clusters can be set to 2 (one anomalous and one normal)
- Summarized by taking averages across an interval of one hour
- Rolling Window Sequences
Key Notes
- Calculate Automatic correlation based on timeseries values
- Identify local maxima
- The seasonal trend identification module
- Data store for Normal data, Anamoly data
- Scoring module
- Human in loop feedback system
Sklearn Models for Supervised Anomaly Detection. Some popular scikit-learn models for supervised anomaly detection include:
- KNeighborsClassifier
- SVC (SVM classifier)
- DecisionTreeClassifier
- RandomForestClassifier
- Interquartile Range
- Isolation Forest
- Median Absolute Deviation
- K-Nearest Neighbours
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