engleski

Determining how to optimise data quality and analysis to ensure precise forecasts

Understanding the importance of ensuring high data quality and assessing the impact of poor have important role in forecasting process. Sensitivity-based method for checking the accuracy of input data and prerequisites for data quality improving are presented on practical cases where input data uncertainty is evident. Representation of analytical expressions for uncertainty is done by contour graphs showing the sensitivity of the forecasted market capacity by logistic model depending on quality of input data. In cases when presented direct assessment of uncertainty shows too high relative errors, judgmental forecasted methods are more appropriate. For such cases, well known logistic and Bass model are re-parameterized by introducing explanatory parameters which can be estimated judgmentally.

Quantitative models for forecasting; Explanatory parameters; Product life cycle; Uncertainty of market capacity

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