Which model ensures forecast quantities are not less than 0?

The linear forecast model is particularly suited for ensuring that forecast quantities do not fall below zero due to its straightforward mathematical foundation, which typically focuses on deriving a linear relationship based on historical data. By establishing a trend line, this model can effectively project future values based on past observations without dipping into negative numbers, as it primarily produces estimates that extend from existing data points.

In contrast, the other models mentioned like the Holt-Winters model, double exponential smoothing model, and Croston's method may not inherently restrict forecasts to non-negative values, as they often incorporate parameters that can lead to negative projections under certain conditions, especially in cases where data patterns do not align well with the underlying assumptions of trend and seasonality. This makes the linear forecast model a reliable choice for scenarios where a guarantee of non-negative outputs is essential.