Differenced data-based “what if” simulation system

A non-transitory computer readable medium, storing instructions for generating simulations based on data, the instructions comprising:

• receiving internal data and external data which affect output data;
• differencing the internal data, the external data, and the output data to create first differenced data;
• grouping the internal data based on select granularity, wherein the select granularity is based on available grouping information contained within the internal data;
• resampling the grouped internal data and the external data based on different sampling frequencies to establish correlation between the internal data, the external data, and the output data;
• training a machine learning model using the first differenced data;
• identifying a time sampling frequency having an optimal correlation distribution among the different sampling frequencies and using the identified time sampling frequency as sampling frequency to prepare the internal data, the external data, and the output data for training the machine learning model;
• receiving scenario input data as input to the machine learning model, wherein the scenario input data comprises conditional data input from a user and/or input data obtained from a separately simulated machine learning data change model, the conditional data input including at least one feature dimension associated with at least one of the internal data or the external data, and the input data including at least one feature dimension associated with at least one of the internal data or the external data;
• and
• generating predicted output based on the scenario input data.