Nathanael C Yoder

US Patent:

20230110117, Apr 13, 2023

Filed:

Sep 28, 2022

Appl. No.:

17/954978

Inventors:

- Mountain View CA, US
Nathanael Christian Yoder - San Francisco CA, US
Tomas Pfister - Redwood Shores CA, US

International Classification:

G06N 3/04
G06N 3/08

Abstract:

Aspects of the disclosure provide for self-adapting forecasting (SAF) during the training and execution of machine learning models trained for multi-horizon forecasting on time-series data. The distribution of time-series data can shift over different periods of time. A deep neural network and other types of machine learning models are trained assuming that training data is independent and identically distributed (i.i.d.). With a computer system configured to execute SAF, the system can, at inference time, update a trained encoder to generate an encoded representation of time-series data capturing features characterizing the current distribution of the input time-series data. The updated encoded representation can be fed into a decoder trained to generate a multi-horizon forecast based on the updated encoded representation of the time-series data. At each instance of inference, the base weights of a trained model can be reused and updated to generate an updated encoded representation for that instance.