A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A multi-expert based machine learning method to determine a blended forecasting model, the method comprising: storing historical data, the historical data including estimates and measurements of a parameter of interest and estimates of critical parameters, the critical parameters determined to be critical to an estimate of the parameter of interest; training a plurality of machine learning models with respective machine learning algorithms using training data that includes a first set of parameter values associated with a first range of time points, the first set of parameter values being obtained from the historical data; obtaining estimates of the parameter of interest with each of the machine learning models using a second set of parameter values associated with a second range of time points, the second set of parameter values being obtained from the historical data; determining, using a processor, a most accurate machine learning model among the machine learning models at each time point in the second range of time points; and determining the blended forecasting model based on the most accurate machine learning model determined for each time point in the second range of time points. 2. The method according to claim 1, wherein each of the machine learning algorithms blends a set of individual models. 3. The method according to claim 2, further comprising obtaining estimates of the input critical parameters from the set of individual models to determine the blended forecasting model. 4. The method according to claim 1, wherein the training the plurality of the machine learning models with the machine learning algorithms includes training with a linear regression, random forest regression, gradient boosting regression tree, support vector machine, or neural network. 5. The method according to claim 1, wherein the determining the most accurate machine learning model at each time point in the second range of time points includes comparing the respective estimate of the parameter of interest obtained with each of the machine learning models at the associated time point with a corresponding measurement of the parameter of interest in the historical data. 6. The method according to claim 1, wherein the determining the blended forecasting model includes selecting one of the machine learning models determined as the most accurate machine learning model as the blended forecasting model. 7. The method according to claim 1, wherein, the determining the blended forecasting model includes correlating, for each time point in the second range of time points, the most accurate machine learning model with corresponding values of the critical parameters in the historical data, wherein the blended forecasting model corresponding with input critical parameters is determined as the most accurate machine learning model correlated with the input critical parameters. 8. The method according to claim 1, wherein the correlating includes training a classification machine learning model. 9. A multi-expert based machine learning system to determine a blended forecasting model, the system comprising: a memory device to store historical data of parameters, the historical data including estimates and measurements of a parameter of interest and estimates of critical parameters determined to be critical to an estimate of the parameter of interest; and a processor configured to train a plurality of learning models with respective machine learning algorithms using training data that includes a first set of parameter values associated with a first range of time points obtained from the historical data, to obtain estimates of the parameter of interest with each of the machine learning models using a second set of parameter values associated with a second range of time points, the second set of parameter values being obtained from the historical data, to determine a most accurate machine learning model among the machine learning models at each time point in the second range of time points, and to determine the blended forecasting model from the most accurate machine learning models. 10. The system according to claim 9, wherein each of the machine learning algorithms blends a set of individual models. 11. The system according to claim 10, wherein the set of individual models estimates the input critical parameters. 12. The system according to claim 9, wherein the machine learning algorithms include training with a linear regression, random forest regression, gradient boosting regression tree, support vector machine, or neural network. 13. The system according to claim 9, wherein the processor compares the respective estimate of the parameter of interest obtained with each of the machine learning models at the associated time point in the second range of time points with a corresponding measurement of the parameter of interest in the historical data to determine the most accurate machine learning model at each time point in the range of time points. 14. The system according to claim 9, wherein for each time point, the processor correlates the most accurate machine learning model with corresponding values of the critical parameters in the second set of parameter values from the historical data, and the processor determines the blended forecasting model as the most accurate machine learning model correlated with input critical parameters. 15. A non-transitory computer program product having computer readable instructions stored thereon which, when executed by a processor, cause the processor to implement a method of determining a blended forecasting model, the method comprising: obtaining historical data, the historical data including estimates and measurements of a parameter of interest and estimates of critical parameters, the critical parameters determined to be critical to an estimate of the parameter of interest; training a plurality of machine learning models with respective machine learning algorithms using training data that includes a first set of parameter values associated with a first range of time points, the first set of parameter values being obtained from the historical data; obtaining estimates of the parameter of interest with each of the machine learning models using a second set of parameter associated with a second range of time points, the second set of parameter values being obtained from the historical data; determining a most accurate machine learning model among the machine learning models at each time point in the second range of time points; and determining the blended forecasting model based on the most accurate machine learning model determined for each time point in the second range of time points. 16. The non-transitory computer program product according to claim 15, wherein each of the machine learning algorithms blends a set of individual models. 17. The non-transitory computer program product according to claim 16, further comprising obtaining estimates of the input critical parameters from the set of individual models to determine the blended forecasting model. 18. The non-transitory computer program product according to claim 15, wherein the training the plurality of the machine learning models with the machine learning algorithms includes training with a linear regression, random forest regression, gradient boosting regression tree, support vector machine, or neural network. 19. The non-transitory computer program product according to claim 15, wherein the determining the most accurate machine learning model at each time point in the second range of time points includes comparing the respective estimate of the parameter of interest obtained with each of the machine learning models at the associated time point with a corresponding measurement of the parameter of interest in the historical data. 20. The non-transitory computer program product according to claim 15, wherein, for each time point, the determining the blended forecasting model includes correlating the most accurate machine learning model with corresponding values of the critical parameters in the historical data, wherein the blended forecasting model corresponding with input critical parameters is determined as the most accurate machine learning model correlated with the input critical parameters.