Forecasting
Showing 20 of 20 projects
An open-source forecasting tool for time series data with multiple seasonality and linear or non-linear growth.
An automatic forecasting procedure for time series data with multiple seasonality and linear or non-linear growth.
Statsmodels: statistical modeling and econometrics in Python
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A Python library for user-friendly forecasting and anomaly detection on time series, from ARIMA to deep neural networks.
An open-source, cross-platform machine learning framework for .NET developers to build, train, and deploy custom ML models.
A Python library for lightning-fast univariate time series forecasting with optimized statistical and econometric models.
A Python library offering scalable and user-friendly implementations of state-of-the-art neural forecasting models.
An open-source Python library for probabilistic time series modeling with both frequentist and Bayesian inference methods.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A Python framework for scalable time series forecasting using machine learning models, designed for production environments.
An R package for tidy time series forecasting with models like ETS and ARIMA, integrated with the tidyverse.
A tidy R package for detecting anomalies in time series data using decomposition and statistical methods.
A Python framework for accessing wind data sources and performing renewable energy forecasting and prediction.
An easy-to-use Python feature store for machine learning, optimized for timeseries data and built on Dask.
A Python SDK for deploying teams of AI research agents to forecast, score, classify, and gather data at scale.
A workflow engine that unifies feature engineering and machine learning using a column-oriented data processing paradigm.
Python package that projects customer retention rates and calculates lifetime value using shifted-beta-geometric distribution fitting.
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