Horizon-based smoothing of forecasting model

A method comprising:

• storing time-series data;
• executing a training iteration for a machine learning model based on one or more parameter values, wherein the executing comprises inputting training data into the machine learning model and outputting predicted values;
• determining error values between the predicted values output by the machine learning model and actual values of the time-series data for a plurality of intervals included in a horizon of the time-series data;
• generating a total error value for the horizon based on the determined error values for the plurality of intervals;
• and
• storing the generated total error value for the horizon.

A non-transitory computer-readable medium comprising instructions which when executed by a processor cause a computer to perform a method comprising:

• storing time-series data;
• executing a training iteration for a machine learning model based on one or more parameter values, wherein the executing comprises inputting training data into the machine learning model and outputting predicted values;
• determining error values between the predicted values output by the machine learning model and actual values of the time-series data for a plurality of intervals included in a horizon of the time-series data;
• generating a total error value for the horizon based on the determined error values for the plurality of intervals;
• and
• storing the generated total error value for the horizon.