Abstract: A computer-implemented system for managing online, event-driven asset network events is disclosed. The system presents role-specific electronic dashboards to different parties via a web application. Prior to a scheduled event, a user selection and associated value are captured and stored within an immutable executable file containing asset attributes and computer-readable instructions. The file is stored in both a system database and on the user device to prevent unauthorized modification. Upon detecting commencement of the scheduled event, the system automatically executes the file, submitting the instructed value across connected platforms and confirming completion of the network operation in accordance with predefined transfer rules. Integrated synchronization and external data interfaces provide real-time updates, secure recordkeeping, and coordinated process management for all stakeholders.

Abstract: The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction quantities for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over starting at a lowest level. For each current level in the iteration, features to include in a quantity forecasting model for the current level are identified. The quantity forecasting model is trained using the identified features. Predicted transaction dates are predicted for the current level by a transaction date prediction model. The quantity forecasting model is used to generate predicted quantity information for the current level for the predicted transaction dates.

Abstract: The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction quantities for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over starting at a lowest level. For each current level in the iteration, features to include in a quantity forecasting model for the current level are identified. The quantity forecasting model is trained using the identified features. Predicted transaction dates are predicted for the current level by a transaction date prediction model. The quantity forecasting model is used to generate predicted quantity information for the current level for the predicted transaction dates.