Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
christophmark/bayesloop की मुख्य विशेषताएं हैं: Time Series।
christophmark/bayesloop के ओपन-सोर्स विकल्पों में शामिल हैं: 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… joaquinamatrodrigo/skforecast — Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation… linkedin/greykite — A flexible, intuitive and fast forecasting library. linkedin/luminol — Anomaly Detection and Correlation library. nixtla/mlforecast — Scalable machine 🤖 learning for time series forecasting.
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
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
A flexible, intuitive and fast forecasting library
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