Abstract: Systems and methods are directed to predicting temporal startup measurements using a machine-trained model. The system determines a training dataset of features associated with different funding, exit, and closure events and corresponding times of the funding, exit, and closure events from historical financial data. A temporal prediction model is trained using the training dataset. The temporal prediction model can comprise a recurrent neural network (e.g., gated recurrent unit). During runtime, the system accesses new data associated with potential future investment opportunities with startups and determines (e.g., compute) company features based, in part, on the new data. The system applies the company features to the temporal prediction model to simultaneously predict a next event and a time of the next event for each startup. A user interface can then be presented that shows the predicted next event and the predicted time of the predicted next event for each startup.

Abstract: Systems and methods are directed to predicting temporal startup measurements using a machine-trained model. The system determines a training dataset of features associated with different funding, exit, and closure events and corresponding times of the funding, exit, and closure events from historical financial data. A temporal prediction model is trained using the training dataset. The temporal prediction model can comprise a recurrent neural network (e.g., gated recurrent unit). During runtime, the system accesses new data associated with potential future investment opportunities with startups and determines (e.g., compute) company features based, in part, on the new data. The system applies the company features to the temporal prediction model to simultaneously predict a next event and a time of the next event for each startup. A user interface can then be presented that shows the predicted next event and the predicted time of the predicted next event for each startup.