Khayati, Mourad Lerner, Alberto Tymchenko, Zakhar Cudré-Mauroux, Philippe 2020 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. application/pdf https://folia.unifr.ch/global/documents/309429 https://folia.unifr.ch/documents/309429/files/2020_Cudre-Mauroux_Mind.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.14778/3377369.3377383 info:eu-repo/semantics/openAccess License undefined Proceedings of the VLDB Endowment. - 2020, vol. 13, no. 5, p. 768-782 time series imputation info:eu-repo/classification/udc/004 Mind the Gap : An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series http://purl.org/coar/resource_type/c_5794