11. A system configured to predict an affective response of a user to a stream of token instances, comprising: a sample generator configured to receive the stream of token instances, and to partition the stream of token instances into consecutive temporal windows of token instances; wherein the temporal windows of token instances are ordered according to their start time; and a sequential machine learning-based predictor configured to receive input data comprising: a vector of values derived from a first temporal window of token instances, and an initial state value derived from a value of a measurement channel of the user taken at time corresponding to start of the first temporal window of token instances; and predict, based on the input data, an affective response of the user to the first temporal window of token instances; and for each successive temporal window of token instances after the first temporal window of token instances, the sequential machine learning-based predictor is further configured to: receive input data comprising: a vector of values derived from the successive temporal window of token instances, and an initial state value derived from prediction of affective response of the user to a temporal window of token instances preceding the successive temporal window of token instances; and to predict, based on the input data, an affective response of the user to the successive temporal window of token instances.
17. The system of claim 11 , wherein the sequential machine learning-based predictor utilizes a neural network.