Universal Time-Series Forecasting with Mixture Predictors

Specificaties
Paperback, blz. | Engels
Springer International Publishing | e druk, 2020
ISBN13: 9783030543037
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Springer International Publishing e druk, 2020 9783030543037
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Samenvatting

The author considers the problem of sequential probability forecasting in the most general 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.

Specificaties

ISBN13:9783030543037
Taal:Engels
Bindwijze:paperback
Uitgever:Springer International Publishing

Inhoudsopgave

Introduction.- Notation and Definitions.- Prediction in Total Variation: Characterizations.- Prediction in KL-Divergence.- Decision-Theoretic Interpretations.- Middle-Case: Combining Predictors Whose Loss Vanishes.- Conditions Under Which One Measure Is a Predictor for Another.- Conclusion and Outlook.

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        Universal Time-Series Forecasting with Mixture Predictors