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Effects of drift and noise on the optimal sliding window size for data stream regression models

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  • Katharina Tschumitschew
  • Frank Klawonn

Abstract

The analysis of non stationary data streams requires a continuous adaption of the model to the relevant most recent data. This requires that changes in the data stream must be distinguished from noise. Many approaches are based on heuristic adaptation schemes. We analyze simple regression models to understand the joint effects of noise and concept drift and derive the optimal sliding window size for the regression models. Our theoretical analysis and simulations show that a near optimal window size can be crucial. Our models can be used as benchmarks for other models to see how they cope with noise and drift.

Suggested Citation

  • Katharina Tschumitschew & Frank Klawonn, 2017. "Effects of drift and noise on the optimal sliding window size for data stream regression models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(10), pages 5109-5132, May.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:10:p:5109-5132
    DOI: 10.1080/03610926.2015.1096388
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