Predicting the outcomes of every process for which an asymptotically accurate stationary predictor exists is impossible

Daniil Ryabko, Boris Ryabko

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2 Citations (Scopus)

Abstract

The problem of prediction consists in forecasting the conditional distribution of the next outcome given the past. Assume that the source generating the data is such that there is a stationary predictor whose error converges to zero (in a certain sense). The question is whether there is a universal predictor for all such sources, that is, a predictor whose error goes to zero if any of the sources that have this property is chosen to generate the data. This question is answered in the negative, contrasting a number of previously established positive results concerning related but smaller sets of processes.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1204-1206
Number of pages3
ISBN (Electronic)9781467377041
DOIs
Publication statusPublished - 28 Sep 2015
Externally publishedYes
EventIEEE International Symposium on Information Theory, ISIT 2015 - Hong Kong, Hong Kong
Duration: 14 Jun 201519 Jun 2015

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2015-June
ISSN (Print)2157-8095

Conference

ConferenceIEEE International Symposium on Information Theory, ISIT 2015
CountryHong Kong
CityHong Kong
Period14.06.201519.06.2015

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