Variability in forecasting outcomes generated by time-series models, such as those employed by the Prophet library, is a common occurrence. This fluctuation stems from the probabilistic nature of these models, which account for uncertainty in historical data and future trends. For instance, a sales forecast might vary slightly on each run, even with identical input data, due to the model’s internal randomness in parameter estimation and simulation.
Understanding the reasons behind these variations is crucial for robust decision-making. Recognizing that a single prediction represents just one possible outcome allows for more informed risk assessment and strategic planning. Historically, forecasting has evolved from deterministic methods to probabilistic ones, embracing the inherent uncertainty in predicting the future. This shift acknowledges that a range of potential outcomes is more realistic than a single point prediction and enables better preparation for various scenarios. Considering the range of possible outcomes also allows for proactive mitigation strategies and the development of contingency plans.
