I have a simulation model of a system which receives a forecast of a time series as input. In my scientific work I would like to examine how the performance of the simulation model behaves in relation to the accuracy of the forecast.
Therefore, I would like to adjust the forecast:
forecast_adjusted = forecast_original.copy() for i in range(len(forecast_adjusted)): forecast_adjusted[i] = forecast_adjusted[i] + p * (actual_data[i] - forecast_adjusted[i])
Where p is a value selected based on the desired improvement or reduction of accuracy.
My question now is: is this a legitimate way to scientifically examine a system with different forecasts? Or do I lose essential characteristics of the forecast?