However, no mechanism was proposed to calculate the size of the observations which should be taken into the forecasting process in an adaptive manner.
The most complex systems, i.e. those that consider more sources of uncertainty in the forecasting process, are those that showed the most reduced expected damages.
These systems differ by the location of the sources of uncertainty, and the total amount of uncertainty they take into account in the forecasting process.
Surveys of business plans to build or reduce inventory have been helpful; econometric methods have also been applied; but inventory investment remains one of the weak links in the forecasting process.
On the contrary, in this paper we aim at showing that, first of all often aggregating and/or disaggregating data in the forecasting process can lead to substantial improvements; second, the choice of the appropriate level of aggregation depends on the underlying demand generation process.
Similar to that of outliers in stationary time series, these non-stationary events will lead to increased level of errors in the forecasting process.
The current issues involve the form of the combining rule, cases with agreement among forecasts, cases with extensive disagreement, dependence, uncertainty about forecast characteristics, instability in the forecasting process, robustness and the role of simple rules, and the role of group interaction.
In the forecasting process and in producing the exclusionary map, worn-out tissue, vacant and abandoned urban lands, and unsuitable land-use maps are coded as development potential and existing green spaces and lands with high potential for natural hazards are coded as development restriction areas.
In the forecasting process, the authors estimated the coefficients from a time series, with one value representing each time point, and a state space model was used to model and forecast these time series coefficients [ 23- 25, 28, 130].
