公开(公告)号：US20230244947A1

公开(公告)日：2023-08-03

申请号：US17843775

申请日：2022-06-17

Applicant: Salesforce, Inc.

Inventor： Gerald Woo ,  Chenghao Liu ,  Doyen Sahoo ,  Chu Hong Hoi

IPC: G06N3/08 ,  G06Q10/04

CPC classification number: G06N3/088 ,  G06Q10/04

Abstract: Embodiments described herein provide a method of forecasting time series data at future timestamps in a dynamic system. The method of forecasting time series data also includes receiving, via a data interface, a time series dataset. The method also includes determining, via a frequency attention layer, a seasonal representation based on a frequency domain analysis of the time series data. The method also includes determining, via an exponential attention layer, a growth representation based on the seasonal representation. The method also includes generating, via a decoder, a time series forecast based on the seasonal representation and the trend representation.

公开(公告)号：US20230409901A1

公开(公告)日：2023-12-21

申请号：US17946363

申请日：2022-09-16

Applicant: Salesforce, Inc.

Inventor： Chenghao Liu ,  Chu Hong Hoi ,  Kun Zhang

IPC: G06N3/08 ,  G06K9/62

CPC classification number: G06N3/08 ,  G06K9/6256

Abstract: Systems and methods for providing a neural network system for time series forecasting are described. A time series dataset that includes datapoints at a plurality of timestamps in an observed space is received. The neural network system is trained using the time series dataset. The training the neural network includes: generating, using an encoder of the neural network system, one or more estimated latent variables of a latent space for the time series dataset; generating, using an auxiliary predictor of the neural network system, a first latent-space prediction result based on the one or more estimated latent variables; transforming, using a decoder of the neural network system, the first latent-space prediction result to a first observed-space prediction result; and updating parameters of the neural network system based on a loss based on the first observed-space prediction result.

公开(公告)号：US20230376734A1

公开(公告)日：2023-11-23

申请号：US17946365

申请日：2022-09-16

Applicant: Salesforce, Inc.

Inventor： Chenghao Liu ,  Chu Hong Hoi ,  Kun Zhang

IPC: G06N3/04

CPC classification number: G06N3/0472 ,  G06N3/0454

Abstract: Systems and methods for providing a neural network system for time series forecasting are described. A time series dataset that includes datapoints at a plurality of timestamps in an observed space is received. A first state-space model of a dynamical system underlying the time series dataset is provided. The first state-space model includes a non-parametric latent transition model. One or more latent variables of a latent space for the time series dataset are determined using the neural network system based on the first state-space model. A first prediction result for the time series dataset is provided by the neural network system based on the estimated latent variables.

公开(公告)号：US20230244943A1

公开(公告)日：2023-08-03

申请号：US17871819

申请日：2022-07-22

Applicant: Salesforce, Inc.

Inventor： Hong-Quang Pham ,  Chenghao Liu ,  Doyen Sahoo ,  Chu Hong Hoi

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/084 ,  G06N3/0472

Abstract: Embodiments provide a framework combining fast and slow learning Networks (referred to as “FSNet”) to train deep neural forecasters on the fly for online time-series fore-casting. FSNet is built on a deep neural network backbone (slow learner) with two complementary components to facilitate fast adaptation to both new and recurrent concepts. To this end, FSNet employs a per-layer adapter to monitor each layer's contribution to the forecasting loss via its partial derivative. The adapter transforms each layer's weight and feature at each step based on its recent gradient, allowing a finegrain per-layer fast adaptation to optimize the current loss. In addition, FSNet employs a second and complementary associative memory component to store important, recurring patterns observed during training. The adapter interacts with the memory to store, update, and retrieve the previous transformations, facilitating fast learning of such patterns.