Time-window based attention long short-term memory network of deep learning

A computer program product for using a time-window based attention long short-term memory (TW-LSTM) network to analyze sequential data with time irregularity, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors, the program instructions executable to:

• split elapsed time into a predetermined number of time windows;
• calculate average values of previous cell states in respective ones of the time windows;
• set the average values as aggregated cell states for the respective ones of the time windows;
• generate attention weights for the respective ones of the time windows;
• calculate a new previous cell state, based on the aggregated cell states and the attention weights for the respective ones of the time windows;
• and
• update a current cell state, based on the new previous cell state.

A computer system for using a time-window based attention long short-term memory (TW-LSTM) network to analyze sequential data with time irregularity, the computer system comprising:

• one or more processors, one or more computer readable tangible storage devices, and program instructions stored on at least one of the one or more computer readable tangible storage devices for execution by at least one of the one or more processors, the program instructions executable to:
• split elapsed time into a predetermined number of time windows;
• calculate average values of previous cell states in respective ones of the time windows;
• set the average values as aggregated cell states for the respective ones of the time windows;
• generate attention weights for the respective ones of the time windows;
• calculate a new previous cell state, based on the aggregated cell states and the attention weights for the respective ones of the time windows;
• and
• update a current cell state, based on the new previous cell state.