Real-time opportunity discovery for productivity enhancement

A computer program product comprising:

• one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:
• program instructions to extract a set of features from time series data, through autoencoding using a neural network, based on one or more non-control variables for the time series data, the one or more non-control variables defining a lack of control by a user on the time series data;
• program instructions to identify one or more operational modes based on the extracted features including a dimensional reduction with a representation learning from the time series data;
• program instructions to identify a neighborhood of a current operational state based on the extracted features, the neighborhood being a dynamic mode within a same operational mode;
• program instructions to compare the current operational state to historical operational states based on the time series data at the same operational mode of the one or more operational modes;
• program instructions to discover an operational opportunity based on the comparison of the current operational state to the historical operational states using the neighborhood;
• program instructions to identify one or more control variables in the same mode which variables are relevant to the current operational state, the one or more control variables defining actions that can be controlled by the user;
• and
• program instructions to recommend an action strategy based on the one or more control variables, the one or more non-control variables, and a target productivity.

A computer system comprising:

• one or more computer processors, one or more computer readable storage media, and program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising:
• program instructions to extract a set of features from time series data, through autoencoding using a neural network, based on one or more non-control variables for the time series data, the one or more non-control variables defining a lack of control by a user on the time series data;
• program instructions to identify one or more operational modes based on the extracted features including a dimensional reduction with a representation learning from the time series data;
• program instructions to identify a neighborhood of a current operational state based on the extracted features, the neighborhood being a dynamic mode within a same operational mode;
• program instructions to compare the current operational state to historical operational states based on the time series data at the same operational mode of the one or more operational modes;
• program instructions to discover an operational opportunity based on the comparison of the current operational state to the historical operational states using the neighborhood;
• program instructions to identify one or more control variables in the same mode which variables are relevant to the current operational state, the one or more control variables defining actions that can be controlled by the user;
• and
• program instructions to recommend an action strategy based on the one or more control variables, the one or more non-control variables, and a target productivity.