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Highlights

Unlike many open-source packages for outlier detection, PyCatcher provides several distinctive features:

  • Automatic Model Selection: PyCatcher automatically detects whether to use an additive or multiplicative decomposition model, ensuring the most accurate detection of outliers based on the characteristics of your data.

  • Dynamic Method Selection Based on Data Size: PyCatcher seamlessly switches between Seasonal Trend Decomposition (for datasets spanning at least two years) and Inter Quartile Range (IQR) for shorter time periods, offering flexibility without manual intervention.

  • Wide Time Frequency Support: Supports multiple time-series frequencies — including daily, weekly, monthly, and quarterly data—without requiring users to pre-process or adjust their datasets.

  • Choice of Different Seasonal Trend Algorithms: Support for outlier detection using various Seasonal Trend Decomposition algorithms (Classical; STL; MSTL).

  • Adaptation for Changing Seasonality : Multiple Seasonal-Trend decomposition using Loess (MSTL) can model seasonality which changes with time.

  • Integrated Diagnostics: PyCatcher includes comprehensive diagnostic tools, enabling users to visualize outliers, trends and seasonal patterns, evaluate data stationarity, and analyze decomposition results.

  • User Interface: Availability of a simple user interface for the users to upload file for outlier detection using IQR.