Kats is a Python framework for time series analysis that provides detection, forecasting, feature extraction, and multivariate analysis capabilities. The toolkit addresses the need for a unified, accessible approach to time series work by combining multiple analysis tasks—from computing key statistics and detecting anomalies to forecasting trends—into a single lightweight package designed for extensibility.
The framework tackles time series analysis by offering modular components for different analytical goals. For forecasting, it includes implementations of established models like Prophet. For anomaly and change-point detection, it provides algorithms such as CUSUM. The toolkit also supports feature extraction from time series data, allowing users to derive meaningful characteristics that can feed into downstream analysis or machine learning pipelines. The self-supervised learning approach documented in the accompanying research enables automatic hyperparameter optimization for forecasting models at scale, reducing manual tuning overhead and improving generalization across diverse datasets.
Teams should adopt Kats if they need a generalist time series toolkit rather than a specialized solution for a single task. It suits organizations performing production forecasting at scale, particularly those managing heterogeneous time series datasets where manual hyperparameter tuning becomes impractical. The framework's lightweight design allows users to install only the components they need, making it suitable for environments with constrained dependencies. The project provides tutorials and examples covering forecasting, detection, and feature extraction, giving adopters clear entry points for common use cases.
The project maintains active continuous integration workflows and welcomes community contributions. Development is organized around a core research-backed algorithm for hyperparameter tuning that has been presented at international forecasting venues and informed subsequent work in automated model selection for time series.
