US 12,223,399 B2
System and method for deep enriched neural networks for time series forecasting
Suleyman Cetintas, Cupertino, CA (US); and Xian Wu, Notre Dame, IN (US)
Assigned to YAHOO ASSETS LLC, Dulles, VA (US)
Filed by YAHOO ASSETS LLC, Dulles, VA (US)
Filed on Oct. 28, 2020, as Appl. No. 17/083,020.
Prior Publication US 2022/0129790 A1, Apr. 28, 2022
Int. Cl. G06N 20/00 (2019.01); G06F 16/903 (2019.01)
CPC G06N 20/00 (2019.01) [G06F 16/903 (2019.01)] 17 Claims
OG exemplary drawing
 
1. A method implemented on at least one machine including at least one processor, memory, and communication platform capable of connecting to a network for machine learning, the method comprising:
receiving input data associated with a time series;
obtaining hidden representations associated with the time series in a feature space;
generating a query vector based on the hidden representations in a query space, wherein the query vector comprises a forward query vector and a backward query vector, each of which corresponds to a linear transformation of the hidden representations;
querying, based on the query vector and across a plurality of time series, relevant historic information related to the time series, wherein the time series is included in the plurality of time series;
aggregating the relevant historic information with the query vector to generate at least one queried pattern vector; and
enriching the hidden representations by aggregating therewith the at least one queried pattern vector to generate enriched hidden representations, wherein
the enriched hidden representations enhance expressiveness of the hidden representations.