| CPC G06N 3/08 (2013.01) [G06N 3/044 (2023.01)] | 15 Claims |
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1. A computer-implemented method comprising:
training a data processing system to generate a plurality of embedding arrays for a set of target dates, each of the plurality of embedding arrays having a first number of dimensions and representing information associated with a respective target date of the set of target dates, the training comprising, for each sequence of dates including training dates from a set of training dates:
receiving, for each training date of a respective sequence of dates, a respective input data array, wherein each respective input data array is received sequentially in a chronological order based on the training dates of the respective sequence of dates, each respective input data array having a second number of dimensions greater than the first number of dimensions and representing values of a predetermined set of date-dependent features;
receiving a target output value corresponding to an evaluation of a predetermined metric for each training date; and
performing an update routine comprising:
processing each respective input data array sequentially received for each training date in the respective sequence of dates using first one or more layers of a neural network to generate an intermediate data array for a last training date in the respective sequence of dates, wherein the intermediate data array has the first number of dimensions,
processing the intermediate data array using second one or more layers of the neural network to generate a network output value,
determining an error between the network output value and the target output value for the last training date in the respective sequence of dates, and
updating values of a set of parameters of the neural network in a direction of a negative gradient of the determined error between the network output value and the target output value,
wherein, when the update routine has been performed for each training date of the set of training dates, the trained first one or more layers are obtained, and
wherein the computer-implemented method further comprises:
generating, by the data processing system, the plurality of embedding arrays using the trained first one or more layers, by processing respective target input data arrays, each respective target input data array corresponding to the respective target date of the set of target dates, wherein, as a result of the processing by the trained first one or more layers, each of the plurality of embedding arrays that corresponds to the respective target date is dependent on the respective input data array for the target date and dates other than the target date in the set of target dates; and
transmitting the plurality of embedding arrays to a remote computing system over a network, for training a forecasting model.
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