Machine-learned state space model for joint forecasting

A computing system configured for joint prediction of future time-series and time-to-event, the computing system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that collectively store:
• a machine-learned state space model comprising at least a first machine-learned function that models a relationship between a past state of the machine-learned state space model and a likelihood of a future intervention associated with the machine-learned state space model and a second machine-learned function that models an effect of a past intervention on a future state of the machine-learned state space model, the machine-learned state space model configured to:
• receive one or more input time series;
• input, responsive to the receiving and based at least in part on the one or more input time series, to the first machine-learned function, data indicative of one or more states of the system at a first time associated with the one or more input time series;
• subsequently receive, from the first machine-learned function based on the data indicative of the one or more states, data indicative of one or more probabilities that one or more future interventions will be performed on the system at a second time later than the first time;
• input, responsive to the receiving, to the second machine-learned function of the machine-learned state space model, the data indicative of the one or more probabilities that the one or more future interventions will be performed on the system at the second time;
• subsequently receive, from the second machine-learned function based on the data indicative of the one or more probabilities that the one or more future interventions will be performed on the system at the second time, data indicative of one or more predicted states of the system at one or more third times later than the second time;
• and
• output, based at least in part on the data indicative of the one or more predicted states, one or more trajectory predictions and a time-to-event prediction, wherein the one or more trajectory predictions comprise a future time series for one or both of future observations of the system or future interventions performed on the system;
• and
• instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:
• inputting the one or more input time series into the machine-learned state space model;
• and
• receiving the one or more trajectory predictions and the time-to-event prediction as an output of the machine-learned state space model.