Abstract: An illustrative embodiment provides a computer-implemented method. The method comprises using a processor set to train a first classification model using a first training dataset. The processor set receives a number of news articles from a plurality of data sources. The processor set classifies the number of news articles using the first classification model to generate a second training dataset. The processor set trains a second classification model using the first training dataset and the second training dataset. The processor set adjusts parameters for the second classification model based on a combination of optimization techniques to generate an improved classification model.

Abstract: Classifying cybersecurity signals from media sources into distinct categories is provided. The method comprises receiving a first data subset comprising data points labeled by subject matter experts according to a predetermined number of specified categories. The data points include information regarding cybersecurity from a set of news articles. The first subset is enriched by applying a random forest algorithm to generate synthetic data points, thereby deriving a second data subset that is augmented from the first subset. The combined first and second data subsets comprise an enhanced training dataset. A BERT model is trained with the enhanced training dataset to classify cybersecurity-related news according to the specified categories. The BERT model utilizes a specialized vector database integrating domain-specific cyber-related terminology and contextual embeddings. The trained BERT model classifies a second set of news articles according to the specified categories.

Abstract: Classifying cybersecurity signals from media sources into distinct categories is provided. The method comprises receiving a first data subset comprising data points labeled by subject matter experts according to a predetermined number of specified categories. The data points include information regarding cybersecurity from a set of news articles. The first subset is enriched by applying a random forest algorithm to generate synthetic data points, thereby deriving a second data subset that is augmented from the first subset. The combined first and second data subsets comprise an enhanced training dataset. A BERT model is trained with the enhanced training dataset to classify cybersecurity-related news according to the specified categories. The BERT model utilizes a specialized vector database integrating domain-specific cyber-related terminology and contextual embeddings. The trained BERT model classifies a second set of news articles according to the specified categories.

Abstract: A method of forecasting is provided. The method comprises forecasting, with a number of univariate models, a number of company-level metrics for a company over a specified time period according to company-specific historical data. A multivariate model is also used to forecast the company-level metrics over the specified time period according to the company-specific historical data and sector-level historical data related to an industry sector to which the company belongs. The forecasts of the univariate models and the multivariate model are combined into an ensemble model, which then forecasts the company-level metrics over the specified time period.