Beyond Models

Abstract

Complex network phenomena -- such as information cascades in online social networks -- are hard to fully observe, model, and forecast. In forecasting, a recent trend has been to forgo the use of parsimonious models in favor of models with increasingly large degrees of freedom that are trained to learn the behavior of a process from historical data. Extrapolating this trend into the future, eventually we would renounce models all together. But is it possible to forecast the evolution of a complex stochastic process directly from the data without a model? In this work we show that model-free forecasting is possible. We present SED, an algorithm that forecasts process statistics based on relationships of statistical equivalence using two general axioms and historical data. To the best of our knowledge, SED is the first method that can perform axiomatic, model-free forecasts of complex stochastic processes. Our simulations using simple and complex evolving processes and tests performed on a large real-world dataset show promising results.