PyTimeTK is a Python time series toolkit (port of R's timetk) providing a unified API for time series analysis, feature engineering, visualization, anomaly detection, and financial technical indicators. It supports pandas, Polars, and cuDF backends via a clean engine abstraction layer, enabling transparent multi-engine execution. The library is structured around modular subpackages: core (summarization, padding, future frames), feature_engineering (lags, rolling, expanding, Fourier, wavelet, spline, holiday signatures), finance (MACD, RSI, Bollinger Bands, regime detection, risk metrics), plot (Plotly/Plotnine/Matplotlib), datasets, cross-validation, and a beta FeatureStore with MLflow integration. All functions follow a consistent augment_* pattern, register as DataFrame/GroupBy methods via pandas_flavor, and expose a .tk accessor for Polars. The codebase demonstrates strong engineering practices: extensive unit tests, CI workflows, versioned dependencies, and a clear API surface.