# An Experimental Evaluation of the Adaptive Sampling Method for Time Series Classification and Clustering

### Muhammad Marwan Muhammad Fuad

#### Abstract

Adaptive sampling is a dimensionality reduction technique of time series data inspired by the dynamic programming piecewise linear approximation. This dimensionality reduction technique yields a suboptimal solution of the problem of polygonal curve approximation by limiting the search space. In this paper, we conduct extensive experiments to evaluate the performance of adaptive sampling in 1-NN classification and k-means clustering tasks. The experiments we conducted show that adaptive sampling gives satisfactory results in the aforementioned tasks even for relatively high compression ratios.

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#### Paper Citation

#### in Harvard Style

Fuad M. (2016). **An Experimental Evaluation of the Adaptive Sampling Method for Time Series Classification and Clustering** . In *Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,* ISBN 978-989-758-173-1, pages 48-54. DOI: 10.5220/0005694600480054

#### in Bibtex Style

@conference{icpram16,

author={Muhammad Marwan Muhammad Fuad},

title={An Experimental Evaluation of the Adaptive Sampling Method for Time Series Classification and Clustering},

booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

year={2016},

pages={48-54},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0005694600480054},

isbn={978-989-758-173-1},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,

TI - An Experimental Evaluation of the Adaptive Sampling Method for Time Series Classification and Clustering

SN - 978-989-758-173-1

AU - Fuad M.

PY - 2016

SP - 48

EP - 54

DO - 10.5220/0005694600480054