The present invention provides a forecasting engine with the ability to minimize prediction error in a preferred direction. It comprises of a receiver configured to receive training data samples. In addition, the forecasting engine includes a building module configured to build a base learner model from the training data samples. In addition, the forecasting engine includes a custom error function that emphasizes prediction error along a pre-configured direction. In addition, the forecasting engine includes an error determination module configured to determine the prediction error made by the base learner model. In addition, the forecasting engine includes an error minimization module configured to construct a new model that has lesser prediction error than the base learner model, where prediction error is as defined by the custom error function. In addition, the forecasting engine includes an iteration module that manages multiple iterations of the error determination module and the error minimization module.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A forecasting engine comprising: a receiver configured to receive training data samples, wherein each sample consists of a set of input attributes and a target values, a building module configured to build a base learner model, the base learner module is configured to predict the target value as a. function of the input attributes; a custom error function which emphasizes prediction error along a pre-configured preferred direction, wherein the direction is chosen in at least one of a positive and a negative direction; an error determination module configured to determine the prediction error made by the base learner model on an independent test dataset; an error minimization module configured to construct a new model which has lesser prediction error than the base learner model, where the prediction error is as defined by the custom error function; and an iteration module configured to manage multiple iterations of the error determination module and the error minimization module, wherein the iteration module continues to iterate till further iterations do not produce models with lesser errors. 2. The forecasting engine as claimed in claim 1, comprising a correction module configured to correct the error. 3. The forecasting engine as claimed in claim 2, wherein the error determination module and error minimization module are run in a loop in order to iteratively reduce the error. 4. The forecasting engine as claimed in claim 1, wherein the base learner module built to predict the target value as a function of the input attributes is a regression analysis module. 5. The forecasting engine as claimed in claim 1, wherein the error determination module uses a sigmoid function to lead the error minimization in a pre-configured direction. 6. The forecasting engine as claimed in claim 1, wherein differentiation technique is used for constructing a new model from the base learner model, which has lesser error than the base learner model as defined by the custom error function. 7. A computer program product for use with a computer, the computer program product comprising a computer usable medium having a computer readable program code embodied therein for minimizing prediction error in a preferred direction in a forecasting technique, the computer program code performing: receiving training data samples, wherein each sample consists of a set of input attributes and a target value which has to be modelled, building a base learner model, wherein the base learner model is configured to predict the target value as a function of the input attributes; predicting error along a pre-configured preferred direction, wherein the direction is chosen in at least one of the positive direction and a negative direction; an error determination module configured to determine the prediction error made by the base learner model on an independent test dataset an error minimization module configured to construct a new model which has lesser prediction error than the base learner model, wherein the prediction error s as defined by the custom error function; and an iteration module which manages multiple iterations of the error determination module and the error minimization module, wherein the iteration module continues to iterate till further iterations do not produce models with lesser errors. 8. The computer program product as claimed in claim 7, comprising a correction module configured to correct the error. 9. The computer program product as claimed in claim 8, wherein the error determination module and error minimization module are run in a loop in order to iteratively reduce the error. 10. The computer program product as claimed in claim 7, wherein the base learner module built to predict the target value as a function of the input attributes is a regression analysis module. 11. The computer program product as claimed in claim 7, wherein the error determination module uses a sigmoid function to lead the error minimization in a pre-configured direction. 12. The computer program product as claimed in claim 7, wherein differentiation technique is used for constructing a new model from the base learner model, which has lesser error than the base learner model as defined by the custom error function.