Online prediction of Exponential decay time series with human-agent application

Ariel Rosenfeld, Joseph Keshet, Claudia V. Goldman, Sarit Kraus

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

11 Scopus citations

Abstract

Exponential decay time series are prominent in many fields. In some applications, the time series behavior can change over time due to a change in the user's preferences or a change of environment. In this paper we present an innovative online learning algorithm, which we name Exponentron, for the prediction of exponential decay time series. We state a regret bound for our setting, which theoretically compares the performance of our online algorithm relative to the performance of the best batch prediction mechanism, which can be chosen in hindsight from a class of hypotheses after observing the entire time series. In experiments with synthetic and real-world data sets, we found that the proposed algorithm compares favorably with the classic time series prediction methods by providing up to 41% improvement in prediction accuracy. Furthermore, we used the proposed algorithm for the design of a novel automated agent for the improvement of the communication process between a driver and its automotive climate control system. Throughout extensive human study with 24 drivers we show that our agent improves the communication process and increases drivers' satisfaction, exemplifying the Exponentron's applicative benefit.

Original languageEnglish
Title of host publicationFrontiers in Artificial Intelligence and Applications
EditorsGal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hullermeier, Virginia Dignum, Frank Dignum, Frank van Harmelen
PublisherIOS Press BV
Pages595-603
Number of pages9
ISBN (Electronic)9781614996712
DOIs
StatePublished - 2016
Event22nd European Conference on Artificial Intelligence, ECAI 2016 - The Hague, Netherlands
Duration: 29 Aug 20162 Sep 2016

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume285
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference22nd European Conference on Artificial Intelligence, ECAI 2016
Country/TerritoryNetherlands
CityThe Hague
Period29/08/162/09/16

Bibliographical note

Publisher Copyright:
© 2016 The Authors and IOS Press.

Funding

We would like to thank the ERC (grant # 267523) for their support in this research.

FundersFunder number
European Research Council267523

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