Core Concepts

augurs is a comprehensive time series analysis library that provides several core capabilities:

Forecasting

Time series forecasting involves predicting future values based on historical patterns. The library supports multiple forecasting methods:

  • MSTL (Multiple Seasonal-Trend decomposition using LOESS)
  • ETS (Error, Trend, Seasonal) models
  • Prophet (Facebook's forecasting tool)
  • Custom models through the Forecaster trait

Clustering

Time series clustering helps identify groups of similar time series within a dataset. Key features include:

  • DBSCAN clustering with DTW (Dynamic Time Warping) distance
  • Flexible distance metrics
  • Parallel processing support for large datasets

Outlier Detection

Outlier detection is the task of identifying one or more time series that deviate significantly from the norm. augurs includes:

  • MAD (Median Absolute Deviation) detection
  • DBSCAN-based outlier detection
  • Customizable sensitivity parameters

Changepoint Detection

augurs re-exports the changepoint crate for detecting changes in time series data:

  • Normal distribution-based changepoint detection
  • Autoregressive Gaussian process changepoint detection

Seasonality Analysis

Understanding seasonal patterns is essential for time series analysis:

  • Automatic period detection
  • Multiple seasonality handling
  • Seasonal decomposition

Data Transformations

The library supports various data transformations:

  • Linear interpolation for missing values
  • Min-max scaling
  • Logarithmic transformation
  • Custom transformations through the Transform trait