Prophet is a time series forecasting library and decomposition tool that uses an additive regression model to predict future values. It functions as an uncertainty estimation tool, calculating confidence intervals and error metrics to quantify the risk associated with future predictions. The project is distinguished by its ability to incorporate human-interpretable parameters for model tuning and its use of Bayesian inference for parameter estimation. It supports the integration of external regressors and special event modeling to account for the impact of holidays and specific dates on forec
Neuralforecast is a neural time series forecasting library designed to predict future values for one or multiple series using deep learning architectures. It functions as a distributed machine learning forecasting framework that enables the training of global models across multiple time series to improve generalization through cross-learning. The project distinguishes itself as a probabilistic forecasting toolkit that produces uncertainty intervals and probability distributions rather than single point estimates. It also includes a hierarchical forecast reconciler to ensure that predictions a
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
statsforecast is a high-performance statistical time series forecasting library designed to generate point forecasts and prediction intervals. It functions as a distributed time series framework that utilizes a C-based forecasting engine and an automated model selector to identify and fit the optimal statistical model for every unique series in a dataset. The system also includes a time series anomaly detector to identify unusual data points by comparing observed values against probabilistic forecast intervals. The project is distinguished by its ability to handle massive-scale parallel forec
A flexible, intuitive and fast forecasting library
Die Hauptfunktionen von linkedin/greykite sind: Time Series, Time Series Analysis.
Open-Source-Alternativen zu linkedin/greykite sind unter anderem: nixtla/neuralforecast — Neuralforecast is a neural time series forecasting library designed to predict future values for one or multiple… unit8co/darts — Darts is a Python time series library designed for forecasting, anomaly detection, and the preprocessing of univariate… alan-turing-institute/sktime — sktime is a machine learning framework designed for time series analysis. It provides a unified interface for… facebook/prophet — Prophet is a time series forecasting library and decomposition tool that uses an additive regression model to predict… nixtla/statsforecast — statsforecast is a high-performance statistical time series forecasting library designed to generate point forecasts… blue-yonder/tsfresh — tsfresh is an automated feature engineering tool and library designed to extract statistical characteristics from raw…