Prediction of Locally Stationary Data Using Expert Advice
Vladimir V'yugin Vladimir Trunov
The problem of continuous machine learning is studied. Within the framework
of the game-theoretic approach, when for calculating the next forecast, no
assumptions about the stochastic nature of the source that generates the data
flow are used -- the source can be analog, algorithmic or probabilistic, its
parameters can change at random times, when building a prognostic model, only
structural assumptions are used about the nature of data generation. An online
forecasting algorithm for a locally stationary time series is presented. An
estimate of the efficiency of the proposed algorithm is obtained.