Selecting forecasting models by machine learning based on analysis of model robustness

A computer program product for selecting predictions made by models with normal and anomalous predictor variable data, 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:

• receive a request for a forecast of a dependent variable in a time domain, the time domain including first respective time periods that have normal labels due to normal predictor variable data and second respective time periods that have anomalous labels due to anomalous predictor variable data;
• retrieve accuracy scores and robustness scores of models, the accuracy scores indicating forecasting accuracy in the first respective time periods and the robustness scores indicating forecasting accuracy in the second respective time periods;
• select dependent variable values predicted by a first model that has highest values of the accuracy scores, for predictions in the first respective time periods;
• and
• select dependent variable values predicted by a second model that has highest values of the robustness scores, for predictions in the second respective time periods.

A computer system for selecting predictions made by models with normal and anomalous predictor variable data, 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:

• receive a request for a forecast of a dependent variable in a time domain, the time domain including first respective time periods that have normal labels due to normal predictor variable data and second respective time periods that have anomalous labels due to anomalous predictor variable data;
• retrieve accuracy scores and robustness scores of models, the accuracy scores indicating forecasting accuracy in the first respective time periods and the robustness scores indicating forecasting accuracy in the second respective time periods;
• select dependent variable values predicted by a first model that has highest values of the accuracy scores, for predictions in the first respective time periods;
• and
• select dependent variable values predicted by a second model that has highest values of the robustness scores, for predictions in the second respective time periods.