公开(公告)号：US20220261651A1

公开(公告)日：2022-08-18

申请号：US17479565

申请日：2021-09-20

Applicant: salesforce.com, inc.

Inventor： Gerald Woo ,  Doyen Sahoo ,  Chu Hong Hoi

IPC: G06N3/08 ,  G06N3/04

Abstract: A multi-view contrastive relational learning framework is provided. In the multi-view contrastive relational learning framework, contrastive learning is augmented with a multi-view learning signal. The auxiliary views guide an encoder of the underlying time series data's main view, by using an inter-sample similarity structure as a learning signal to learn representations which encode information from multiple views.

公开(公告)号：US20230376746A1

公开(公告)日：2023-11-23

申请号：US17939085

申请日：2022-09-07

Applicant: Salesforce.com, inc.

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

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/0481

Abstract: Embodiments described herein provide a time-index model for forecasting time-series data. The architecture of the model takes a normalized time index as an input, uses a model, g_φ, to produce a vector representation of the time-index, and uses a “ridge regressor” which takes the vector representation and provides an estimated value. The model may be trained on a time-series dataset. The ridge regressor is trained for a given g_φ to reproduce a given lookback window. g_φ is trained over time-indexes in a horizon window, such that g_φ and the corresponding ridge regressor will accurately predict the data in the horizon window. Once g_φ is sufficiently trained, the ridge regressor can be updated based on that final g_φ over a lookback window comprising the time-indexes with the last known values. The final g_φ together with the updated ridge regressor can be given time-indexes past the known values, thereby providing forecasted values.