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Sequential learning of multi-state autoregressive time series

Published: 09 October 2015 Publication History

Abstract

Modeling and forecasting streaming data has fundamental importance in many real world applications. In this paper, we present an online model selection technique that can be used to model non-stationary time series in a sequential manner. Multi-state autoregressive (AR) model is used to describe non-stationary time series, and a dynamic algorithm is applied to learn the states at each time step. The proposed technique estimates a candidate AR filter from the most recent data points at every time step, and checks whether starting a new state significantly decreases prediction error or not. To that end, a time-varying threshold is compared with the reduction in the prediction error caused by postulating a new AR filter. The threshold is calculated by sampling and clustering uniformly distributed stable AR filters. Numerical simulations show that the proposed algorithm accurately estimates the state transitions with a small delay.

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  • (2018)Model Selection in Online Learning for Times Series ForecastingAdvances in Computational Intelligence Systems10.1007/978-3-319-97982-3_7(83-95)Online publication date: 11-Aug-2018

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cover image ACM Conferences
RACS '15: Proceedings of the 2015 Conference on research in adaptive and convergent systems
October 2015
540 pages
ISBN:9781450337380
DOI:10.1145/2811411
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 October 2015

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Author Tags

  1. autoregressive process
  2. multi-state process
  3. online update
  4. sequential learning

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  • Research-article

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  • Defense Advanced Research Projects Agency (DARPA)

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RACS '15
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RACS '15 Paper Acceptance Rate 75 of 309 submissions, 24%;
Overall Acceptance Rate 393 of 1,581 submissions, 25%

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  • (2018)Model Selection in Online Learning for Times Series ForecastingAdvances in Computational Intelligence Systems10.1007/978-3-319-97982-3_7(83-95)Online publication date: 11-Aug-2018

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