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).

pytorch-forecasting is a deep learning library for time series forecasting, providing implementations of state-of-the-art models such as Temporal Fusion Transformer, N-BEATS, N-HiTS, DeepAR, PatchTST, TiDE, TimeXer, and many others. Built on PyTorch and PyTorch Lightning, it offers a complete pipeline from data preprocessing (TimeSeriesDataSet, encoders, samplers) through model training (with GPU support, callbacks, hyperparameter tuning via optuna) to prediction and evaluation (point, quantile, and distributional metrics). The library supports multivariate forecasting, static and time-varying covariates, categorical features, and multiple targets. A major v2 refactoring is underway, introducing a new data module layer and simplified package interface, though the v2 API is explicitly marked as experimental and unstable. The codebase is well-tested with automated integration tests across all models and metrics, and employs a composition pattern for metrics (supporting weighting, aggregation, and distribution losses) and a layered architecture for models (base classes, package containers, building blocks).