{"repo":"Nixtla/mlforecast","free":true,"listed":false,"github":"https://github.com/Nixtla/mlforecast","clone":"git clone https://github.com/Nixtla/mlforecast.git","description":"Scalable machine 🤖 learning for time series forecasting.","language":"Python","stars":1269,"topics":["forecast","forecasting","machine-learning","lightgbm","xgboost","dask","python","time-series"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"mlforecast Machine Learning 🤖 Forecast Scalable machine learning for time series forecasting mlforecast is a framework to perform time series forecasting using machine learning models, with the option to scale to massive amounts of data using remote clusters. Install PyPI pip install mlforecast conda-forge conda install -c conda-forge mlforecast For more detailed instructions you can refer to the installation page. Quick Start 1. Get Started with this quick guide. 2. Follow this end-to-end walkthrough for best practices. Videos - Overview Sample notebooks - m5 - m5-polars - m4 - m4-cv - favorita - VN1 Why? Current Python alternatives for machine learning models are slow, inaccurate and don’t scale well. So we created a library that can be used to forecast in production environments. MLForecast includes efficient feature engineering to train any machine learning model (with fit and predict methods such as sklearn ) to fit millions of time series. Features - Fastest implementations of feature engineering for time series forecasting in Python. - Out-of-the-box compatibility with pandas, polars, spark, dask, and ray. - Probabilistic Forecasting with Conformal Prediction. - Support for exogenous variables and static covariates. - Familiar sklearn syntax: .fit and .predict . Missing something? Please open an issue or write us in Examples and Guides 📚 End to End Walkthrough: model training, evaluation and selection for multiple time series. 🔎 Probabilistic Forecasting: use Confor","default_branch":null,"files":null,"tree":[],"storefront":"/r/Nixtla","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Nixtla/mlforecast/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}