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The main features of angus924/rocket are: Time Series Analysis.
Open-source alternatives to angus924/rocket include: blue-yonder/tsfresh — tsfresh is an automated feature engineering tool and library designed to extract statistical characteristics from raw… facebook/prophet — Prophet is a time series forecasting library and decomposition tool that uses an additive regression model to predict… alan-turing-institute/sktime — sktime is a machine learning framework designed for time series analysis. It provides a unified interface for… awslabs/gluonts — GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for… alkaline-ml/pmdarima — A statistical library designed to fill the void in Python's time series analysis capabilities, including the… fraunhoferportugal/tsfel — An intuitive library to extract features from time series.
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
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
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
tsfresh is an automated feature engineering tool and library designed to extract statistical characteristics from raw time series data. It transforms sequential data into tabular datasets, converting time series into a flat format where each row represents a unique entity and columns represent extracted features. The project distinguishes itself through a parallel data processing framework that distributes heavy computational workloads across multiple CPU cores. It also implements hypothesis-based feature selection to identify the most predictive characteristics and filter out irrelevant ones