Universal Time-Series Forecasting with Mixture Predictors
Ryabko, Daniil.
Universal Time-Series Forecasting with Mixture Predictors [electronic resource] / by Daniil Ryabko. - 1st ed. 2020. - VIII, 85 p. 1 illus. online resource. - SpringerBriefs in Computer Science, 2191-5776 . - SpringerBriefs in Computer Science, .
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.
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.
9783030543044
10.1007/978-3-030-54304-4 doi
Computer science.
Artificial intelligence.
Computer science--Mathematics.
Theory of Computation.
Artificial Intelligence.
Mathematics of Computing.
QA75.5-76.95
004.0151
Universal Time-Series Forecasting with Mixture Predictors [electronic resource] / by Daniil Ryabko. - 1st ed. 2020. - VIII, 85 p. 1 illus. online resource. - SpringerBriefs in Computer Science, 2191-5776 . - SpringerBriefs in Computer Science, .
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.
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.
9783030543044
10.1007/978-3-030-54304-4 doi
Computer science.
Artificial intelligence.
Computer science--Mathematics.
Theory of Computation.
Artificial Intelligence.
Mathematics of Computing.
QA75.5-76.95
004.0151