Abstract: The invention provides a computer-implemented method of training an approximation architecture for a forecasting system including performing a pre-training stage comprising steps of: obtaining training samples each including an input value for a first plurality of input variables and corresponding parameter values of the functions; obtaining dependencies between different variables of the first plurality of input variables; determining, based on the obtained dependencies, dimensionality-reducing rules for determining a second plurality of input variables, wherein there are fewer degrees of freedom in the second plurality than in the first plurality; and, determining, by applying the dimensionality-reducing rules to the training samples, modified training samples including input values for at least some of the second plurality of variables and corresponding parameter values approximating the functions.

Abstract: The invention provides a computer-implemented method of training an approximation architecture for a forecasting system including performing a pre-training stage comprising steps of: obtaining training samples each including an input value for a first plurality of input variables and corresponding parameter values of the functions; obtaining dependencies between different variables of the first plurality of input variables; determining, based on the obtained dependencies, dimensionality-reducing rules for determining a second plurality of input variables, wherein there are fewer degrees of freedom in the second plurality than in the first plurality; and, determining, by applying the dimensionality-reducing rules to the training samples, modified training samples including input values for at least some of the second plurality of variables and corresponding parameter values approximating the functions.

Abstract: The invention provides a non-homogeneous machine learning architecture for approximating an overall function as a composition of at least a first approximation function and a second approximation function. The non-homogeneous machine learning architecture comprises a neural network architecture for providing the first approximation function, the neural network architecture being configured to receive a plurality of input values for the overall function as an input to the first approximation function, and to apply the first approximation function to the received input values to generate a plurality of neural network output values.

Abstract: A computer-implemented method of determining an approximated value of a parameter in a first domain is described. The parameter is dependent on one or more variables which vary in a second domain, and the parameter is determined by a function which relates sets of values of the one or more variables in the second domain to corresponding values in the first domain.

Abstract: A computer-implemented method of determining an approximated value of a parameter in a first domain is described. The parameter is dependent on one or more variables which vary in a second domain, and the parameter is determined by a function which relates sets of values of the one or more variables in the second domain to corresponding values in the first domain.

Abstract: A computer-implemented method of determining an approximated value of a parameter in a first domain is described. The parameter is dependent on one or more variables which vary in a second domain, and the parameter is determined by a function which relates sets of values of the one or more variables in the second domain to corresponding values in the first domain.

Abstract: A computer-implemented method of determining an approximated value of a parameter in a first domain is described. The parameter is dependent on one or more variables which vary in a second domain, and the parameter is determined by a function which relates sets of values of the one or more variables in the second domain to corresponding values in the first domain.