From 3cee8ada5f53b9624c60cf8b458c672b64438f81 Mon Sep 17 00:00:00 2001 From: Vinta Chen Date: Fri, 2 Oct 2026 11:37:54 +0800 Subject: [PATCH] feat: add statsforecast to Machine Learning > Time Series Forecasting The use case had no fast classical statistical forecasting library (ARIMA, ETS, Theta). Co-Authored-By: Claude --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index a51a7a82..20f6eda5 100644 --- a/README.md +++ b/README.md @@ -210,6 +210,7 @@ _Libraries for Machine Learning. Also see [awesome-machine-learning](https://git - [catboost](https://github.com/catboost/catboost) - A fast, scalable, high performance gradient boosting on decision trees library. - Time Series Forecasting - [prophet](https://github.com/facebook/prophet) - A tool for producing forecasts for time series with multiple seasonality and trend changes. + - [statsforecast](https://github.com/Nixtla/statsforecast) - Fast statistical forecasting models such as ARIMA, ETS, and Theta, compiled with numba. - [timesfm](https://github.com/google-research/timesfm) - A pretrained foundation model from Google Research for time-series forecasting, with non-commercial default weights. ### Natural Language Processing