Nonparametric sequential prediction of time series
  Time series prediction covers a vast field of every-day statistical applications in medical, environmental and economic domains. In this paper we develop nonparametric prediction strategies based on the combination of a set of 'experts' and show the universal consistency of these strategies under a minimum of conditions. We perform an in-depth analysis of real-world data sets and show that these nonparametric strategies are more flexible, faster and generally outperform ARMA methods in terms of normalized cumulative prediction error. 
G\'erard Biau, Kevin Bleakley, L\'aszl\'o Gy\"orfi and Gy\"orgy   Ottucs\'ak