sktime is a large, actively developed Python library that provides a unified scikit-learn-style estimator interface for time series machine learning. It is not a single-purpose application or service; it is a framework with a central registry, a datatype/mtype conversion system, tag-based estimator metadata, and a sophisticated automated test suite that generates per-estimator tests from scenarios. The dominant character is application/library, though it has strong research-data aspects: bundled datasets, benchmark suites, and algorithm implementations drawn from the time-series literature (forecasting, classification, clustering, change-point detection, transformations, alignment, distances/kernels, and parameter estimation).
ThymeBoost is a Python library for time series forecasting that applies a gradient boosting approach to decompose and predict trend, seasonality, and exogenous components. Unlike traditional additive models, it iteratively fits these components on residuals across multiple rounds, allowing flexible mixing of estimators (e.g., ARIMA for trend, Fourier for seasonality, OLS for exogenous). The library exposes a clean API through the `ThymeBoost` class with methods for fitting, predicting, optimizing hyperparameters, ensembling, and online learning. It also includes an experimental `AutoForecast` class and a set of unit tests for trend models. While it is packaged as a reusable library (application), the repository includes Jupyter notebooks and sample datasets, giving it a hybrid character that also supports data exploration and research use cases. The codebase demonstrates solid software engineering with abstract base classes for each component type, operator overloading for composing models, and a well-structured boosting loop. However, it shows signs of single-developer maintenance: incomplete error handling, commented-out code, and experimental features. The dependencies are reasonably current, but Python 3.7 compatibility may limit adoption on newer runtimes.