Abstract: A computer-implemented method includes, for each of a set of training dates: receiving, for each of a sequence of dates including the training date, an input data array representing values of a predetermined set of date-dependent features; receiving a target output corresponding to an evaluation of a predetermined metric at the training date; and performing an update routine including processing the input data array for each date using first layers of a neural network, processing a resulting intermediate data array using second layers of the neural network to generate a network output, and updating values of parameters of the neural network in in a direction of a negative gradient of an error between the target output and the network output. The data processing system is then arranged to generate an embedding array by processing an input data array for each of a given sequence of dates using the first layers of the neural network.

Abstract: A computer-implemented method includes, for each of a set of training dates: receiving, for each of a sequence of dates including the training date, an input data array representing values of a predetermined set of date-dependent features; receiving a target output corresponding to an evaluation of a predetermined metric at the training date; and performing an update routine including processing the input data array for each date using first layers of a neural network, processing a resulting intermediate data array using second layers of the neural network to generate a network output, and updating values of parameters of the neural network in in a direction of a negative gradient of an error between the target output and the network output. The data processing system is then arranged to generate an embedding array by processing an input data array for each of a given sequence of dates using the first layers of the neural network.