Methods and systems for generating predictions based on time series data using an ensemble modeling

A method for using an ensemble machine learning model to generate predictions based on time series data, the method comprising:

• receiving a first feature input, wherein the first feature input represents a first set of times series data;
• inputting the first feature input into a first ensemble model component and a second ensemble model component;
• receiving a first output from the first ensemble model component and a second output from the second ensemble model component;
• generating a second feature input based on the first output and the second output;
• inputting the second feature input into a final ensemble model component to determine a final output, wherein the final ensemble model component comprises a plurality of ensemble model components used to generate predictions subject to a constraint comprising imposing an upper bound on over-estimation frequency and an upper bound on a maximum overestimation value;
• and
• generating for display, on a user interface, a prediction based on the final output.

A non-transitory, computer-readable medium for generating hierarchical data structures based on crowdsourced data featuring non-homogenous metadata, comprising instructions that, when executed by one or more processors, cause operations comprising:

• receiving a first feature input, wherein the first feature input represents a first set of times series data;
• inputting the first feature input into a first ensemble model component and a second ensemble model component;
• receiving a first output from the first ensemble model component and a second output from the second ensemble model component;
• generating a second feature input based on the first output and the second output;
• inputting the second feature input into a final ensemble model component to determine a final output, wherein the final ensemble model component comprises a plurality of ensemble model components used to generate predictions subject to a constraint comprising imposing an upper bound on over-estimation frequency and an upper bound on a maximum overestimation value;
• and
• generating for display, on a user interface, a prediction based on the final output.