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.

公开(公告)号：US20240127214A1

公开(公告)日：2024-04-18

申请号：US17956531

申请日：2022-09-29

Applicant: THE TORONTO-DOMINION BANK

Inventor： MATTHEW CARLTON FREDERICK WANDER ,  HOLLY HEGLIN ,  MING JIAN PAN

IPC: G06Q20/20

CPC classification number: G06Q20/20

Abstract: Computational systems and methods are provided to automatically assess residual characteristics of an existing machine learning model to identify and determine suboptimal pockets and augmentation strategies. A computing system, device and method for optimizing a machine learning model for performing predictions is provided. The computing device performs sub-optimal pocket identification on an existing machine learning algorithm by residual analysis to calculate an error. The computing device utilizes the residual as a target for an ensemble tree model and automatically generates a set of interpretable rules from the tree based ensemble model that contribute to the suboptimal pockets. The rules indicating relationships between features and interactions as well as values for the sub-optimal pockets. The computing device determines optimizations for improving the machine learning model based on the interpretable computer-implemented rules.