Which forecasting model is typically more suitable for stable environments where past data is consistent?

The moving average model is particularly well-suited for stable environments where the data is consistent and historical patterns can reliably predict future trends. This model works by averaging a set number of previous data points, providing a straightforward calculation that smooths out fluctuations. In an environment where conditions do not fluctuate significantly, this model can effectively capture the ongoing trends without being overly impacted by random variations or outliers.

In contrast, exponential smoothing may apply weighing factors to more recent data, which can be beneficial in environments that are experiencing trends or shifts. The causal model requires the identification of relationships between variables, which may not be necessary in a stable environment where past data alone is sufficient for forecasting. The subjective model relies on expert opinions and judgments rather than historical data, making it less applicable in situations where data consistency is a primary consideration.

Therefore, the moving average model’s reliance on consistent historical data makes it the most appropriate choice for stable forecasting scenarios.