Abstract: According to an embodiment, a method for generating a forecast of a timeseries is disclosed. The method comprises receiving a set of features comprising data and timeseries to be used by each of a plurality of prediction models for generating the forecast. Further, the method comprises generating using the set of features, a plurality of forecast results based on an ensemble of the plurality of prediction models. Furthermore, the method comprises optimizing the plurality of forecast results associated with a respective forecast module. Additionally, the method comprises probabilistically combining the outputs of the plurality of optimization modules. Moreover, the method comprises outputting a final forecast based on the combination of the at least two forecast results.

Abstract: A system for generating a forecast including a classifier module for receiving from a user, at least one feature and classifying the at least one feature into a plurality of priority groups based on a user preference. The system further includes an artificial intelligence (AI) forecast module in communication with the classifier module for processing the plurality of priority groups with at least one feature. The AI forecast module derive a learning from classification of the at least one feature into the plurality of priority groups; and generate the forecast based on the learning.

Abstract: Method and system is disclosed for forecasting demand with respect to an entity. The method comprises receiving a plurality of input data-sets associated with time-series data, wherein each of said data-sets refers a time-based variation of one or more variables in accordance with a designated time-interval. At least one transformation-result is generated by transforming time-intervals of at least one input dataset based on a plurality of time interval transformation models. A plurality of first intermediate forecast results are predicted based on a plurality of demand forecasting models from the at-least one transformation result. An aggregated result is generated from the plurality of the first intermediate forecast results through an ensemble-model to thereby render said aggregated result as a final prediction result.