Titlebook: Universal Time-Series Forecasting with Mixture Predictors; Daniil Ryabko Book 2020 Springer Nature Switzerland AG 2020 Time Series.Forecas

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原諒

Prediction in Total Variation: Characterizations,possible to provide complete characterizations of those sets of measures . for which predictors exist (realizable case). The non-realizable case turns out to be somewhat degenerate as the asymptotic error in total variation is either 0 or 1. These and related results are exhibited in this chapter.

不近人情

2191-5768 setting, where the observed data may exhibit an arbitrary form of stochastic dependence. All the results presented are theoretical, but they concern the foundations of some problems in such applied areas as machine learning, information theory and data compression.978-3-030-54303-7978-3-030-54304-4Series ISSN 2191-5768 Series E-ISSN 2191-5776

PALL

Introduction,., after which the process continues sequentially. We are interested in constructing predictors . whose conditional probabilities .(?|.., …, ..) converge (in some sense) to the “true” .-conditional probabilities .(?|.., …, ..), as the sequence of observations increases (.?→.). We would also like this convergence to be as fast as possible.

袖章

蒙太奇

Conditions Under Which One Measure Is a Predictor for Another,orem .): absolute continuity is preserved under summation with arbitrary measure as follows directly from its definition (Definition .). For KL divergence, it is guaranteed by (.). Here we show that for other losses this is not necessarily the case.

STRIA

編輯才信任

Notation and Definitions, …, ... We consider stochastic processes (probability measures) on . where . is the sigma-field generated by the (countable) set . of cylinders, . where the words .. take all possible values in .. We use . for expectation with respect to a measure ..

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