User guide

Welcome to the user guide of Skchange. This guide provides a high-level overview of the library, its design and core concepts. It also aims to give a wide range of usage examples.

• Getting started
  • Installation
  • Change detection
    • Detect changes in the mean
    • Detect changes in a continuous piecewise linear trend
    • Detect sparse changes in a high-dimensional mean vector
    • Detect changes in a linear regression model
  • Segment anomaly detection
    • Detect segment anomalies in the mean
    • Detect segment anomalies in multivariate data and identify the anomalous variables
    • Detect segment anomalies in the covariance matrix
  • Penalty calibration
    • Penalty curve
    • Empirical scores distribution
• Concepts
  • Change detectors
    • Segment anomaly detectors
    • Advanced outputs
    • Scikit-learn compatibility
  • Interval scorers
    • Cost
    • Change score
    • Saving
    • Transient score
  • Penalties
    • Penalty arrays
• Change detection
  • Introduction to change detection
    • Task
    • Design
    • Interface
• Segment anomaly detection
  • Segment anomaly detection
    • The task
• Tuning
  • Penalty calibration
• Interval Scorers
  • Interval scorers
    • Cost
    • Change score
    • Anomaly scores
    • General interval scorer API
    • Custom interval scorers