초록

A method of predicting at least one future value of a time series of data using a neural network comprising the steps of:

대표청구항 ▼

[ What is claimed is:] [1.] A method of predicting at least one future data value of a time series of data using a neural network so as to manage resources in a communications network, the method comprising the steps of:(i) inputting a plurality of data values of the time series into the neural netw

[ What is claimed is:] [1.] A method of predicting at least one future data value of a time series of data using a neural network so as to manage resources in a communications network, the method comprising the steps of:(i) inputting a plurality of data values of the time series into the neural network;(ii) inputting, with said data values, time information relating to those values;(iii) obtaining outputs from the neural network said outputs comprising predicted future values of the time series based on said data values and on said time information; and(iv) allocating network resources corresponding to said predicted future values;wherein said step (i) of inputting a plurality of values of the time series into the neural network comprises the step of determining a number of values of the time series that are required to be input to the neural network, said step of determining a number of values comprising the steps of:(v) forming a set of first vectors wherein each first vector is the same size and each first vector comprises a number of sequential values of the time series;(vi) forming a set of second vectors, wherein each second vector is the same size, and each second vector comprises a number of sequential values of the time series, and wherein the first and second vectors are different sizes;(vii) for each first vector selecting another of the first vectors as a first neighbour where a first measure of similarity between each first vector and its first neighbour is less than a threshold value;(viii) for each second vector selecting another of the second vectors as a second neighbour where a second measure of similarity between each first vector and its second neighbour is less than a threshold value, and wherein each second neighbour corresponds to a first neighbour;(ix) determining the number of false neighbours by comparing each first neighbour with its corresponding second neighbour; and(x) determining the number of values that are required to be input to the neural network according to a first vector size for which a threshold number of false neighbours are obtained.