Catalyst
PyTorch framework for Deep Learning R&D focusing on reproducibility and rapid experimentation.
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StatsForecast
4.7
Free StatsForecast is praised for its speed, accuracy, and scalability in time series forecasting.
Lightning fast forecasting with statistical and econometric models.
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Refined 3/4/2026
About StatsForecast
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.
Core Capabilities
StatsForecast is a Python library designed for high-performance time series forecasting.
Main Tasks
Model Evaluation
Explore all tools that specialize in model evaluation. This domain focus ensures StatsForecast delivers optimized results for this specific requirement.
