Verfahren und System zum Trainieren eines Neuronalen Netzwerks zur Vorhersage Multivariater Daten

Details

Veröffentlichungs-Nr.

EP4560532

Aktenzeichen

EP24172811

Anmeldetag

26. April 2024

Veröffentlichung

28. Mai 2025

Rechtsraum

EP

IPC

G06N3/044G06N3/045H04W8/00H04W28/00

Abstract

A computer-implemented method for training a neural network to forecast multivariate data in a forecast location in a network comprising inputting a dataset from the forecast location and one or more adjacent locations, the dataset comprising spatio-temporal characteristics of each location and multivariate data recorded at each location, determining a longest time-series sequence length of the dataset for which an occurrence frequency of the longest time series sequence length appearing in the dataset is higher than a threshold number of the dataset, a time-series sequence length indicating a total length of consecutive time steps with complete data in the dataset, training a forecast location neural network based on the determined longest time series sequence length to encode the multivariate data from the forecast location into a forecast location vector, training for each of the one or more adjacent locations an adjacent location neural network based on the determined longest time series sequence length to encode the multivariate data from each of the one or more adjacent locations each into an adjacent location vector, combining the one or more adjacent location vectors into a combined adjacent location vector, composing the forecast location vector and combined adjacent location vector into a final combined vector, and decoding the final combined vector to generate a forecast from the dataset for the forecast location.

Anmelder

Firma

FUJITSU LIMITED

Land

🇯🇵 Japan

Fachgebiete