A Fuzzy Logic based Trend Impact Analysis method

All Trend Impact Analysis (TIA) algorithms in literature conduct the analysis based on direct estimates provided by experts for the probability of occurrence of an unprecedented event as an input to the algorithm. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using Fuzzy Logic. We postulate that in some cases it is better not to estimate the probability of occurrence of an unprecedented event directly; but rather estimate it indirectly via its attributes, using Fuzzy Logic. The core idea of the paper is to customize the generic process of reasoning with Fuzzy Logic by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes, especially when they reach certain threshold values.