An enhanced approach for Trend Impact Analysis

Decision makers in governments, corporations and institutions all need to forecast the future. Usually, traditional quantitative forecasting techniques are applied for this purpose. But the limitation of such methods is well known since all quantitative methods that are built solely on historical data (whether time-series or causal methods) produce forecasts by extrapolating such data into the future ignoring the effects of unprecedented future events that could cause deviation from the original surprise-free forecast if they were to occur. In the meanwhile, pure qualitative methods that don't utilize historical data miss its sound foundation. In the field of future studies, attempts are often made to combine quantitative and qualitative approaches using various hybrid methods such as Trend Impact Analysis. This paper introduces an advanced algorithm to enhance Trend Impact Analysis that adds another level of sophistication to the current algorithm. This advanced algorithm takes into account not only the impact of unprecedented future events' occurrences on the future trend, but also the different severity degrees with which the event might occur. This idea of severity degrees is novel, and its implementation is the main contribution of this paper.