Abstract: According to an embodiment, a method for generating textual features corresponding to text documents from a raw dataset is disclosed. The method includes preprocessing the text documents and determining topic probability scores (TPS) and confidence scores (CS) using unsupervised and supervised machine learning models, respectively. The combination of TPS and CS is used to generate a compound distribution score (CDS), which forms a comprehensive representation of the output of the machine learning models. The determined TPS, CS, and CDS are then used to generate a set of textual features, which serve as independent variables for a forecasting model.

Abstract: The present disclosure relates to a method for generating a knowledge graph. The method includes determining a causal chain of events indicating a cause-and-effect relationship among entities within the input data based on a causal expression. Further, the method includes assigning attribute labels such as a topic label, a sentiment label, and a temporal label to the entities using a Natural Language Processing (NLP) technique. Further, the method includes creating nodes indicating a collection of entities having the assigned attribute labels. Furthermore, the method includes generating a knowledge graph based on clustering the nodes. The knowledge graph indicates a visual depiction of the causal chain of events such that the nodes are interlinked through a directional edge representing the causal chain of events. In the method, the generated knowledge graph along with the assigned attribute labels is retrieved based on at least one of a user-query input or parameter filters.

Abstract: According to an embodiment, a method for generating textual features corresponding to text documents from a raw dataset is disclosed. The method includes preprocessing the text documents and determining topic probability scores (TPS) and confidence scores (CS) using unsupervised and supervised machine learning models, respectively. The combination of TPS and CS is used to generate a compound distribution score (CDS), which forms a comprehensive representation of the output of the machine learning models. The determined TPS, CS, and CDS are then used to generate a set of textual features, which serve as independent variables for a forecasting model.

Abstract: According to an embodiment, a method for determining feature importance in an ensemble model including a plurality of Machine Learning (ML) models is disclosed. The method includes receiving a dataset comprising input features and a forecast result. The method also includes generating a ranking-based feature list based on the input features. Further, the method includes generating a feature importance output based on the ranking-based features lists. Furthermore, the method includes determining a weightage value corresponding to each of the plurality of ML models based on an accuracy value associated with the corresponding machine learning model. The method also includes determining a weightage-based feature importance value corresponding to each input feature corresponding to the feature importance output based on the determined weightage value corresponding to each ML model responsible for the corresponding input feature in the feature importance output.

Abstract: According to an embodiment, a method for generating and explaining trend forecast of a timeseries with measures of quality of explainability is disclosed. The method comprises receiving a target variable and a set of relevant feature(s) corresponding to the variable. The method comprises performing a classification for the target variable, wherein the classification indicates classifying the target variable into a one or more states. Further, the method comprises determining a state transition matrices for each timestamp and design appropriate functions to model and quantify the trend forecast via a state transition score. The state transition score indicates transition between the corresponding states, wherein states may be obtained through suitable encoding of the target variable, and generating and explaining trend forecast based on the state transition.

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.