Overview
StatsForecast is a Python library designed for high-performance time series forecasting. It leverages statistical and econometric models, including ARIMA, ETS, CES, and Theta, optimized with numba for speed. The architecture is built for scalability, enabling efficient fitting of millions of time series. It supports integration with Spark, Dask, and Ray for distributed computing. StatsForecast provides probabilistic forecasting, confidence intervals, exogenous variables, and anomaly detection. It is compatible with sklearn syntax, making it easy to use. Key value propositions include speed, accuracy, and scalability, making it suitable for production environments and benchmarking. It allows users to forecast in production environments or as benchmarks. The library offers an extensive set of models that efficiently fit millions of time series.