Modelling conditional variance function in industrial data: A case study

We study the suitability of different modelling methods for joint prediction of mean and variance based on large data sets. We review the approaches to the modelling of conditional variance function that are capable of handling a problem where conditional variance depends on about 10 explanatory variables and training dataset consists of 100 000 observations. We present a promising approach for neural network modelling of mean and dispersion. We compare different approaches in predicting the mechanical properties of steel in two case data sets collected from the production line of a steel plate mill. As a conclusion we give some recommendations concerning the modelling of conditional variance in large datasets.