公开(公告)号：US20240394513A1

公开(公告)日：2024-11-28

申请号：US18671875

申请日：2024-05-22

Applicant: Google LLC

Inventor： Rajat Sen ,  Shaan Karan Mathur ,  Abhimanyu Das ,  Weihao Kong ,  Qi Yu ,  Andrew Bradford Leach

IPC: G06N3/0455

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing long-term forecasting using multi-layer perceptron neural networks. One of the methods includes obtaining time series data; and processing the time series data to generate a respective predicted time series value for each of a plurality of future time points in a horizon sequence, comprising: processing the time series data using an encoder multi-layer perceptron (MLP) neural network to generate an encoded representation of the time series data; and processing at least the encoded representation of the time series data using a decoder MLP neural network to generate a respective predicted time series value for each of the plurality of future time points.

公开(公告)号：US20240119265A1

公开(公告)日：2024-04-11

申请号：US18373417

申请日：2023-09-27

Applicant: Google LLC

Inventor： Sercan Omer Arik ,  Yihe Dong ,  Qi Yu ,  Rui Wang

IPC: G06N3/0455 ,  G06N3/08

CPC classification number: G06N3/0455 ,  G06N3/08

Abstract: Aspects of the disclosure provide a deep sequence model, referred to as Koopman Neural Forecaster (KNF), for time series forecasting. KNF leverages deep neural networks (DNNs) to learn the linear Koopman space and the coefficients of chosen measurement functions. KNF imposes appropriate inductive biases for improved robustness against distributional shifts, employing both a global operator to learn shared characteristics, and a local operator to capture changing dynamics, as well as a specially-designed feedback loop to continuously update the learnt operators over time for rapidly varying behaviors. KNF achieves superior performance on multiple time series datasets that are shown to suffer from distribution shifts.