We look at a stochastic time-varying optimization problem and we formulate online algorithms to find and track its optimizers in expectation. The algorithms are derived from the intuition that standard prediction and correction steps can be seen as a dynamical system and a measurement equation, respectively, yielding the notion of filter design. The optimization algorithms are then based on an extended Kalman filter in the unconstrained case, and on a linear matrix inequality condition in the constrained case. Some special cases and variations are discussed, and supporting numerical results are presented from real data sets in ride-hailing scenarios. The results are encouraging, especially when predictions are accurate, a case which is often encountered in practice when historical data is abundant.