Applications of Neural Networks in Forecasting
Artificial Neural Networks have become objects of everyday use ... although few people are aware of it. Their superior performance in optical character recognition, speech recognition, signal filtering in computer modems etc. have established NN as an accepted model & method. However, neural networks have not yet been established as a valid and reliable method in the business forecasting domain, either on a strategic, tactical or operational level. Following we present selected applications of NN which have demonstrated their applicability in specific scenarios.
Considering forecasting objectives, we must differentiate between predictive classification tasks where the forecasted values are class memberships or the probabilities of belonging to certain class, i.e. binary predictors, and regression tasks, where the forecasted value represent a single number of metric scale, e.g. sales, temperature, rainfall etc., as in regression problems. Following we will refer to this as forecasting as opposed to classification.
In forecasting applications, many classification problems also encompass the "prediction" of a categorial or nominal scaled variable. In order to distinguish the distinct modelling approaches and preprocessing required, we consider forecasting applications where the predictor is of metric scale being regression or point prediction based (in the demand planning area simply denoted as sales or demand forecasting). Consequently, a rise / fall-predictions as in financial modelling of buy-hold-strategies would receive consideration as under classification tasks due to their nominal predictors..
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