statsforecast
A Python library for lightning-fast univariate time series forecasting with optimized statistical and econometric models.
What is statsforecast?
StatsForecast is a Python library for lightning-fast univariate time series forecasting using statistical and econometric models. It provides optimized implementations of models like AutoARIMA, ETS, CES, and Theta, designed to be significantly faster and more accurate than existing alternatives. The library solves the problem of slow and inefficient forecasting in Python, enabling scalable analysis on millions of time series.
Data scientists, analysts, and engineers who need to perform high-volume, production-grade time series forecasting or require efficient benchmarks for model comparison. It's particularly valuable for those working with large collections of univariate series.
Developers choose StatsForecast for its exceptional speed and accuracy, often outperforming popular libraries like pmdarima, statsmodels, and Prophet by orders of magnitude. Its unique selling point is the combination of a comprehensive model battery with out-of-the-box scalability for distributed computing frameworks.
Overview
Lightning ⚡️ fast forecasting with statistical and econometric models.
Use Cases
Best For
- High-volume forecasting of millions of univariate time series
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