Mind the Gap : An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series
Université de Fribourg
- Khayati, Mourad University of Fribourg
- Lerner, Alberto University of Fribourg
- Tymchenko, Zakhar University of Fribourg
- Cudré-Mauroux, Philippe University of Fribourg
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2020
Published in:
- Proceedings of the VLDB Endowment. - 2020, vol. 13, no. 5, p. 768-782
English
Recording sensor data is seldom a perfect process. Failures in power, communication or storage can leave occasional blocks of data missing, affecting not only real-time monitoring but also compromising the quality of near- and off-line data analysis. Several recovery (imputation) algorithms have been proposed to replace missing blocks. Unfortunately, little is known about their relative performance, as existing comparisons are limited to either a small subset of relevant algorithms or to very few datasets or often both. Drawing general conclusions in this case remains a challenge. In this paper, we empirically compare twelve recovery algorithms using a novel benchmark. All but two of the algorithms were re-implemented in a uniform test environment. The benchmark gathers ten different datasets, which collectively represent a broad range of applications. Our benchmark allows us to fairly evaluate the strengths and weaknesses of each approach, and to recommend the best technique on a use-case basis. It also allows us to identify the limitations of the current body of algorithms and suggest future research directions.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département d'informatique
- Language
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- English
- Classification
- Computer science and technology
- License
- License undefined
- Identifiers
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- RERO DOC 330481
- DOI 10.14778/3377369.3377383
- Persistent URL
- https://sonar.ch/global/documents/309429
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