Conference paper (in proceedings)

Mind the Gap : An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series

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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
  • English
Classification
Computer science
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/309429
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