Choosing the right point forecast is not about finding the most accurate model — it is about knowing what error you are trying to minimize. Using a log-normal time series where the true distribution is known, this post shows how MSE, MAE, and MAPE each lead to a mathematically different optimal forecast, and why aligning your training loss with your evaluation metric — and both with your business decision — is the step most forecasters skip.
