grey-forecaster

What problem does it solve?

GM(1,1) provides short-term forecasts when data are scarce or incomplete, turning as few as four data points into usable predictions.

Core Features & Use Cases

• Small-sample forecasting (4-10 points) for quick insights.
• Step-by-step GM(1,1) workflow: 1-AGO, mean sequence construction, parameter estimation, IAGO, accuracy checks, and forecasting.
• Use Case: forecast quarterly sales with only a few years of data or monitor emerging phenomena with limited history.
• Easy integration with Python data processing stacks (NumPy) and visualization for validation.

Quick Start

Create a Python data list, initialize GreyForecaster with the data, call fit(), then call predict(n) to forecast n periods. Example: data = [4.2, 4.5, 5.1, 5.8] model = GreyForecaster(data) model.fit() print(model.predict(5))