Mean-field Continuous Sequence Predictors
Keywords: Mean-field graphon games, Mean-field games as continuous sequence prediction, Mean-field Neural SDEs
Abstract: We propose a novel class of neural differential equation models called mean-field continuous sequence predictors (MFPs) for efficiently generating continuous sequences with potentially infinite-order complexity. To address complex inductive biases in time-series data, we employ mean-field dynamics structured through carefully designed graphons. By reframing time-series prediction as mean-field games, we utilize a fictitious play strategy integrated with gradient-descent techniques. This approach exploits the stochastic maximum principle to determine the Nash equilibrium of the system. Both empirical evidence and theoretical analysis underscore the unique advantages of our MFPs, where a collective of continuous predictors achieves highly accurate predictions and consistently outperforms benchmark prior works.
Primary Area: learning on time series and dynamical systems
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Submission Number: 1699
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