Global context explainers for artificial intelligence (ai) systems using multivariate timeseries data

A computer program product, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by at least one processor to perform operations for:

• receiving predictions for multivariate timeseries data;
• generating feature importance weights from the predictions using a feature-based local explainer, wherein each of the feature importance weights is associated with a time period and a corresponding data source of timeseries data of the multivariate timeseries data;
• generating a dataset using the feature importance weights, wherein the dataset includes, for each time period and the corresponding data source, a label indicating whether the feature importance weight is one of positive and negative;
• generating one or more global explanations using the dataset and a directly interpretable rule-based explainer, wherein the one or more global explanations indicate how the predictions change at particular times in the multivariate timeseries data based on values from the corresponding data source;
• and
• performing an action based on the global explanations.

A computer system, comprising:

• one or more processors, one or more computer-readable memories and 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 via at least one of the one or more computer-readable memories, to perform operations comprising:
• receiving predictions for multivariate timeseries data;
• generating feature importance weights from the predictions using a feature-based local explainer, wherein each of the feature importance weights is associated with a time period and a corresponding data source of timeseries data of the multivariate timeseries data;
• generating a dataset using the feature importance weights, wherein the dataset includes, for each time period and the corresponding data source, a label indicating whether the feature importance weight is one of positive and negative;
• generating one or more global explanations using the dataset and a directly interpretable rule-based explainer, wherein the one or more global explanations indicate how the predictions change at particular times in the multivariate timeseries data based on values from the corresponding data source;
• and
• performing an action based on the global explanations.