"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

August 25, 2021

Anamoly Reads

Ref - Link

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
More Reads


Keep Reading!!!

No comments: