公开(公告)号：US20240211732A1

公开(公告)日：2024-06-27

申请号：US18213624

申请日：2023-06-23

Applicant: THE TORONTO-DOMINION BANK

Inventor： MATTHEW CARLTON FREDERICK WANDER ,  HARSHUL VARMA ,  HOLLY HEGLIN ,  MING JIAN PAN ,  ABINAV RAMESH SUNDARARAMAN

IPC: G06N3/0455 ,  G06N3/0442 ,  G06N3/0464

CPC classification number: G06N3/0455 ,  G06N3/0442 ,  G06N3/0464

Abstract: Methods, systems and techniques for multivariate time series forecasting are provided. A dataset is obtained that corresponds to a multivariate time series data for a multivariate time series forecasting task. A particular machine learning architecture is used for the forecasting using an artificial neural network and deep learning. The machine learning architecture includes an autoencoder configured and trained on itself moved forward in time to generate autoencoder layers to analyse seasonality and co-variance information of the multivariate input dataset in a future time frame and an autoregressor to generate autoregressor layers to analyse trend information of the multivariate input dataset in a future time frame; and a layer merger for merging the one or more autoregressor layers and one or more autoencoder layers to form a set of merged layers representative of a multivariate time series forecast using the machine learning model in the future time frame.