Analytic forecasting focuses on the use of quantitative data to make predictions about the future. This type of forecasting relies heavily on historical information as well as mathematical models such as regression analysis, time series analysis, and causal modeling. The major advantage of this method is its ability to identify patterns in large amounts of data that facilitate more accurate predictions. Additionally, analytic forecasts can often provide a high degree of confidence since they rely on hard evidence rather than subjective opinion.
The downside of analytic forecasting is that it requires significant time and resources to collect the necessary input data and develop an appropriate model for predicting outcomes accurately. Furthermore, because all predictive models involve certain assumptions about how variables interact with each other in real-world situations these assumptions may not always hold true thus resulting in less reliable predictions than expected. Finally, sophisticated knowledge in statistics and mathematics is required when using analytical forecating methods which many decision makers do not possess making this approach difficult to implement without additional help from experts who understand analytics techniques better than most people do..
For example, if multiple different analysts have different beliefs regarding what might happen over a certain period then their respective forecasts will likely result in wildly divergent results making it impossible for decision makers to choose one prediction over another with complete certainty as there might be no way determine which forecasted outcome was more accurate at least until after the fact once actual observations could provide confirmation either way . As such nonanalytical forecasts tend deliver less accuracy despite being faster easier forms of prediction albeit at greater risk overall due partly lack specificity available when using analytic techniques .
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