Training a neural network model across multiple domains

A method comprising:

• accessing, by a processor, first training data relating to a first domain, the first training data having a first time series of data comprising first sequential data values that vary over time;
• accessing, by the processor, second training data relating to a second domain, the second training data having a second time series of data comprising second sequential data values that vary independently from the first sequential data values over time;
• generating, by the processor, a first plurality of sequences from the first training data, the first plurality of sequences being generated to include an ordered subset of sequential data values defined by a sliding temporal window over the first time series of data such that adjacent sequences share overlapping data values;
• generating, by the processor, a second plurality of sequences from the second training data, the second plurality of sequences being generated to include an ordered subset of sequential data values defined by a second sliding temporal window over the second time series of data such that adjacent sequences share overlapping data values;
• appending, by the processor, the first plurality of sequences and the second plurality of sequences to generate an appended input data relating to the first domain and the second domain, wherein the appended input data comprises a concatenation of the first plurality of sequences and the second plurality of sequences into a unified training dataset that preserves temporal ordering of each sequence and associates each sequence with a domain identifier to train a single machine-learning model to learn feature relationships shared across the first domain and the second domain;
• and
• providing, by the processor, the appended input data to a neural network to train the single machine-learning model trained to make predictions in the first domain and/or the second domain.

A non-transitory computer readable medium storing instructions that, when executed by a processor, causes the processor to:

• access first training data relating to a first domain, the first training data having a first time series of data comprising first sequential data values that vary over time;
• access second training data relating to a second domain, the second training data having a second time series of data comprising second sequential data values that vary independently from the first sequential data values over time;
• generate a first plurality of sequences from the first training data, the first plurality of sequences being generated to include an ordered subset of sequential data values defined by a sliding temporal window over the first time series of data such that adjacent sequences share overlapping data values;
• generate a second plurality of sequences from the second training data, the second plurality of sequences being generated to include an ordered subset of sequential data values defined by a second sliding temporal window over the second time series of data such that adjacent sequences share overlapping data values;
• append the first plurality of sequences and the second plurality of sequences to generate an appended input data relating to the first domain and the second domain, wherein the appended input data comprises a concatenation of the first plurality of sequences and the second plurality of sequences into a unified training dataset that preserves temporal ordering of each sequence and associates each sequence with a domain identifier to train a single machine-learning model to learn feature relationships shared across the first domain and the second domain;
• and
• provide the appended input data to a neural network to train the single machine-learning model trained to make predictions in the first domain and/or the second domain.