Abstract: A method, apparatus, system, and computer program product for method for dynamically managing an ontology for classifying data is provided. The ontology is generated from the classifications of a plurality of entities. Models are generated that predict classifications according to the ontology and natural language descriptions of the entities. Unclassified entities are modelled by according to the models to identify at least one classification within the ontology. Responsive to identifying a plurality of probable classifications within the ontology, a new classification is generated based on the plurality of probable classifications, an updated ontology is generated that includes the new classification, and the unclassified entity is classified according to the new classification.

Abstract: Testing prototype indices is provided. The method comprises receiving input of a number of historical index values and performing a number of Monte Carlo simulations based on the historical index values to predict future index values. A number of attributes of the historical index values are calculated, and the Monte Carlo simulations are correlated with the attributes to identify a filtered subset of predicted future index values having an accuracy above a defined threshold. The filtered subset of predicted future index values are fed into a deep neural network (DNN) that outputs a confidence score for each of the filtered subset of predictive future index values.

Abstract: A method for point-in-time visualization system management is provided. The method includes receiving organizational structure queries from user interfaces, creating organizational hierarchy database queries from the organizational structure query, and converting the results of those queries into entity change parameters lists and organizational tree structures for display on the user interface. The displayed organizational tree structure and entity change parameters can be filtered via controls available on the user interface.

Abstract: A method, apparatus, system, and computer product for monitoring emission data transmitted over a network. A computer system collects emission data received from sensor devices over the network. The computer system compares the emission data to a set of thresholds for the emission data to form a comparison between the emission data and the set of thresholds. The computer system changes a collection of additional emission data from the sensor devices over the network based on the comparison between the emission data and the set of thresholds.

Abstract: A method, system, and product for multi-class text classification. The method includes receiving an uncoded shipping container description comprising an uncoded text; and assigning a predicted HS code segment to the uncoded container description. Assigning includes cleaning the uncoded text of the uncoded container description by removing 1-2 letter words, numbers and symbols from the text; tokenizing the cleaned uncoded text to define a plurality of unencoded tokens by parsing the cleaned uncoded text into single words and bigrams; summing the scores for each of a plurality of HS code segments associated with each of the plurality of unencoded tokens across all of the plurality of unencoded tokens; and determining the predicted HS code segment based on the highest summation. The method can include receiving an uncoded shipping container description comprising an uncoded text; and assigning a predicted HS code segment to the uncoded container description.

Abstract: A method of textual data analysis is provided. The method comprises parsing text data extracted from transcripts related to a number of companies. Intermediate metrics are created comprising numerical representations of the parsed text data and derivations. The intermediate metrics are then combined into different combinations comprising headline analytics. A machine learning model tests each headline analytic for standalone predictive efficacy. Headline analytics with standalone predictive efficacies above a first threshold are selected and then tested for additive predictive efficacy to determine if the selected headline analytic incrementally increases the predictive efficacy of a preexisting economic analytic above a second threshold.

Abstract: A method, apparatus, system, and computer program code for intelligent data discovery with dynamic ontology are provided. According to one illustrative embodiment, the method using a number of processors to perform the steps of: identifying a set of data items in unstructured content using a dynamic data schema populated from a dynamic ontology; and responsive to identifying a data item that is not recognized in the data schema: storing the data item with labels; generating a weight for the data item; and responsive to the weight exceeding a threshold, updating the schema to include the data item that was not recognized.

Abstract: A framework for an automated news recommendation system for financial analysis. The system includes the automated ingestion, relevancy, clustering, and ranking of news events for financial analysts in the capital markets. The framework is adaptable to any form of input news data and can seamlessly integrate with other data used for analysis like financial data.

Abstract: A method, system, and computer program product for analyzing financial risk of a financial security. A video is displayed by the computer system on a mobile display system. Object detection is performed on the video to identify a fixed asset in the video. The computer system identifies a financial security supported by the fixed asset identified in the video. An overlay of financial data about the financial security is displayed in association with the fixed asset in the video. The financial data includes a financial model of the financial security and an assumption of the financial model. The assumption of the financial model is updated based on a status of the fixed asset in the video. The financial risk of the financial security is analyzed based on the financial model and the assumption that was updated.

Abstract: A method, apparatus, system, and computer program code for forecasting a data anomaly to a supply chain. A plurality of data records is identified for a plurality of entities. The data records include import records and export records. The data fields in the data records are categorized into generic field types. The generic field types include numeric fields, categorical fields, and date fields. For each of the plurality of entities, an entity-specific model is constructed for forecasting imports and exports based on the generic field types. The entity-specific model for each of the plurality of entities is combined into a global supply chain model. Based on the global supply chain model, a data anomaly is forecast to a supply chain that is associated with a particular entity.

Abstract: A method is provided that comprises collecting financial data points and normalizing the data points into a time series. The time series is merged into aggregates according to sliding windows that comprise different time periods and a sliding step increment. A periodic change in an increasing and convex transformation is computed for each aggregate, and multi-dimensional time-delayed coordinate embedding is applied to each aggregate. The sliding windows are applied to the time-delayed aggregates, and time series of variances and point clouds are derived within each sliding window. Persistence homologies and time series norms are computed for the point clouds, and the time series norms are correlated with the time series of variances. A warning of an impending financial crisis is output if the correlation of the time series norms with the time series of variances exceeds a predefined threshold.