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A Python package for time series classification
The main features of johannfaouzi/pyts are: Time Series Analysis.
Projects with overlapping indexed features include: awslabs/gluonts — GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for… blue-yonder/tsfresh — tsfresh is an automated feature engineering tool and library designed to extract statistical characteristics from raw… alan-turing-institute/sktime — sktime is a machine learning framework designed for time series analysis. It provides a unified interface for… angus924/rocket. alkaline-ml/pmdarima — A statistical library designed to fill the void in Python's time series analysis capabilities, including the… facebook/prophet — Prophet is a time series forecasting library and decomposition tool that uses an additive regression model to predict…
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
sktime is a machine learning framework designed for time series analysis. It provides a unified interface for performing time series forecasting, classification, and anomaly detection, integrating these capabilities into a standardized toolkit compatible with the scikit-learn API. The framework allows for the construction of complex analysis workflows through model pipelining and ensemble-based aggregation. It uses adapter-based integration to wrap external time series libraries, providing a single entry point for diverse algorithmic implementations. Its capabilities cover temporal data tran
GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for building, training, and evaluating neural network architectures that predict future values as probability distributions to quantify uncertainty. The project distinguishes itself by supporting zero-shot forecasting and integrating diverse modeling approaches, including deep probabilistic neural networks and wrappers for external statistical libraries such as Prophet and R forecast. It implements specialized architectural primitives like causal convolutions and invertible residual n