grey-forecaster

Small-sample forecasting in Python uses the GM(1,1) model to turn 4-10 data points into usable short-term predictions. You initialize the model with your data list, call fit(), and use predict(n) to forecast future periods.

GM(1,1) is a grey forecasting model that generates predictions from scarce data. It works by applying a step-by-step process: 1-AGO, mean sequence construction, parameter estimation, IAGO, and accuracy checks to produce forecasts from non-seasonal, monotonic trends.

Yes, small-sample forecasting with GM(1,1) runs entirely locally requiring only Python 3.x and NumPy. It needs no external services, though you can optionally add Matplotlib to visualize and validate the resulting time series predictions.

Use GM(1,1) for rapid scenario analysis when you have only 4-10 data points with monotonic trends in non-seasonal contexts. It is specifically designed for situations with limited history where traditional forecasting methods require larger datasets.

No, GM(1,1) forecasting is designed for non-seasonal contexts with monotonic trends. It is best suited for short-term forecasting of emerging phenomena or quarterly sales where data is scarce, incomplete, and exhibits a consistent directional trend.

The GM(1,1) model requires a minimum of 4 data points to perform its core calculations: 1-AGO, mean sequence construction, and parameter estimation. This small-sample threshold allows it to generate forecasts from extremely limited historical data.