Systems and methods for time series analysis using attention models

A system for analyzing and modeling a multivariate time series data based on an attention computation, comprising:

• a controller;
• and
• a tangible, non-transitory memory configured to communicate with the controller, the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the controller, cause the controller to perform operations comprising:
• capturing, by the controller, dependencies across different variables through input embedding;
• mapping, by the controller, an order of a sample appearance to a randomized lookup table via positional encoding;
• capturing, by the controller, dependencies within a plurality of a self-attention mechanisms, each self-attention mechanism of the plurality of self-attention mechanisms capturing dependencies within a single sequence of each self-attention mechanism;
• determining, by the controller, a range of dependency to consider for each position being analyzed within the single sequence of each self-attention mechanism;
• obtaining, by the controller, a plurality of attention weightings to other positions within the single sequence through computation of an inner product, each of the plurality of attention weightings obtained within the single sequence of each self-attention mechanism;
• utilizing, by the controller, the plurality of attention weightings to acquire a plurality of vector representations for a position;
• masking, by the controller, the single sequence of each self-attention mechanism to enable causality;
• employing, by the controller, a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation from the plurality of vector representations;
• utilizing, by the controller, a linear layer to obtain logits from the single vector representation;
• and
• using, by the controller, a final prediction layer whose type depends on a specific task.

An article of manufacture including a tangible, non-transitory computer-readable storage medium having instructions stored thereon that, in response to execution by a processor, cause the processor to perform operations comprising:

• capturing, by the processor, dependencies across different variables of a multivariate time series data through input embedding;
• mapping, by the processor, an order of a sample appearance to a randomized lookup table via positional encoding;
• capturing, by the processor, dependencies within a plurality of self-attention mechanisms, each self-attention mechanism of the plurality of self-attention mechanisms capturing dependencies within a single sequence of each self-attention mechanism;
• determining, by the processor, a range of dependency to consider for each position being analyzed within the single sequence of each self-attention mechanism;
• obtaining, by the processor, a plurality of attention weightings to other positions within the single sequence through computation of an inner product, each of the plurality of attention weightings obtained within the single sequence of each self-attention mechanism;
• utilizing, by the processor, the plurality of attention weightings to acquire a plurality of vector representations for a position;
• masking, by the processor, the single sequence of each self-attention mechanism to enable causality;
• employing, by the processor, a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation from the plurality of vector representations;
• utilizing, by the processor, a linear layer to obtain logits from the single vector representation;
• and
• using, by the processor, a final prediction layer whose type depends on a specific task.