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skchange

  • User guide
  • API reference
  • Developer guide
  • Releases
  • GitHub
  • User guide
  • API reference
  • Developer guide
  • Releases
  • GitHub

Section Navigation

  • Getting started
  • Concepts
  • Change detection
    • Introduction to change detection
  • Segment anomaly detection
    • Segment anomaly detection
  • Tuning
    • Penalty calibration
  • Interval Scorers
    • Interval scorers
  • User guide

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

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