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STATSFORECAST
Lightning-fast statistical forecasting with classical econometric models
Apache-2.0
ABOUT
Classical statistical forecasting methods like ARIMA, ETS, and Theta are well-established but their standard Python implementations are slow for large-scale use. StatsForecast provides highly optimized, parallelized implementations of these models using Numba and distributed computing, achieving orders of magnitude speedup over traditional implementations while maintaining statistical rigor.
INSTALL
pip install statsforecastINTEGRATION GUIDE
1. Forecast thousands of time series simultaneously using parallel statistical models for inventory planning
2. Generate baseline forecasts using ARIMA and ETS for comparison with machine learning and deep learning approaches
3. Perform fast anomaly detection by comparing actual values against statistically modeled confidence intervals
4. Run large-scale backtesting and cross-validation of statistical models across hundreds of time series
TAGS
pythontime-seriesforecastingstatisticsarimaeconometricsnixtla