{"repo":"Nixtla/statsforecast","free":true,"listed":false,"github":"https://github.com/Nixtla/statsforecast","clone":"git clone https://github.com/Nixtla/statsforecast.git","description":"Lightning ⚡️ fast forecasting with statistical and econometric models.","language":"Python","stars":4872,"topics":["time-series","statistics","forecasting","arima","econometrics","machine-learning","python","exponential-smoothing","ets","baselines"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"Nixtla Statistical ⚡️ Forecast Lightning fast forecasting with statistical and econometric models StatsForecast offers a collection of widely used univariate time series forecasting models, including automatic ARIMA , ETS , CES , and Theta modeling optimized for high performance. It also includes a large battery of benchmarking models. Installation You can install StatsForecast with: or Vist our Installation Guide for further instructions. Quick Start Minimal Example Get Started quick guide Follow this end-to-end walkthrough for best practices. Why? Current Python alternatives for statistical models are slow, inaccurate and don't scale well. So we created a library that can be used to forecast in production environments or as benchmarks. StatsForecast includes an extensive battery of models that can efficiently fit millions of time series. Features Fastest and most accurate implementations of AutoARIMA , AutoETS , AutoCES , MSTL and Theta in Python. Out-of-the-box compatibility with Spark, Dask, and Ray. Probabilistic Forecasting and Confidence Intervals. Support for exogenous Variables and static covariates. Anomaly Detection. Familiar sklearn syntax: .fit and .predict . Highlights Inclusion of exogenous variables and prediction intervals for ARIMA. 20x faster than pmdarima . 1.5x faster than R . 500x faster than Prophet . 4x faster than statsmodels . 1,000,000 series in 30 min with ray. Replace FB-Prophet in two lines of code and gain speed and accuracy. Check the experimen","default_branch":null,"files":null,"tree":[],"storefront":"/r/Nixtla","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Nixtla/statsforecast/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."}