System and method for deep enriched neural networks for time series forecasting

Machine readable and non-transitory medium having information recorded thereon for machine learning, wherein the information, when read by a machine, causes the machine to perform:

• 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.

A system for machine learning, comprising:

• a relevant historic information query engine implemented on a processor and configured for
• 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, and
• 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;
• a graph based historic information aggregator implemented on the processor and configured for aggregating the relevant historic information with the query vector to generate at least one queried pattern vector;
• and
• a feature aggregator implemented on the processor and configured for 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.