A system for evaluating a plurality of faults in an operation of a machine at a set of future instances of time forming a prediction horizon, comprising: an input interface configured to accept, during the operation of the machine, input data including current time-series data collected from multiple sensors (104a, 104b, 104c) measuring the operation of the machine over a period of time including multiple time instances up to a current instance of time; a set of current set points for the operation of the machine within the period of time; and a set of future set points of the operation of the machine for a set of future instances of time forming a prediction horizon; a memory configured to store a neural network including a first subnetwork sequentially connected with a sequence of second subnetworks such that an output of one subnetwork is an input to a subsequent subnetwork, wherein there is a second subnetwork for each of the future instance of time within the prediction horizon, wherein the first subnetwork accepts the current time-series data and the current setpoints, wherein each of the second subnetworks accepts the output of a preceding subnetwork, an internal state of the preceding subnetwork, and a future setpoint for a corresponding future instance of time, and wherein each of the second subnetworks outputs an individual prediction of each fault of a plurality of faults at the corresponding future instance of time; a processor configured to submit the input data received from the input interface to the neural network to estimate individual predictions of each fault of the plurality of faults at each of the future instances of time; and an output interface to output the individual predictions of each of the faults. an input interface configured to accept, during the operation of the machine, input data including current time-series data collected from multiple sensors (104a, 104b, 104c) measuring the operation of the machine over a period of time including multiple time instances up to a current instance of time; a set of current set points for the operation of the machine within the period of time; and a set of future set points of the operation of the machine for a set of future instances of time forming a prediction horizon; current time-series data collected from multiple sensors (104a, 104b, 104c) measuring the operation of the machine over a period of time including multiple time instances up to a current instance of time; a set of current set points for the operation of the machine within the period of time; and a set of future set points of the operation of the machine for a set of future instances of time forming a prediction horizon; a memory configured to store a neural network including a first subnetwork sequentially connected with a sequence of second subnetworks such that an output of one subnetwork is an input to a subsequent subnetwork, wherein there is a second subnetwork for each of the future instance of time within the prediction horizon, wherein the first subnetwork accepts the current time-series data and the current setpoints, wherein each of the second subnetworks accepts the output of a preceding subnetwork, an internal state of the preceding subnetwork, and a future setpoint for a corresponding future instance of time, and wherein each of the second subnetworks outputs an individual prediction of each fault of a plurality of faults at the corresponding future instance of time; a processor configured to submit the input data received from the input interface to the neural network to estimate individual predictions of each fault of the plurality of faults at each of the future instances of time; and an output interface to output the individual predictions of each of the faults.