Khayati, Mourad Arous, Ines Tymchenko, Zakhar Cudré-Mauroux, Philippe 2020-11-01 With the emergence of the Internet of Things (IoT), time series streams have become ubiquitous in our daily life. Recording such data is rarely a perfect process, as sensor failures frequently occur, yielding occasional blocks of data that go missing in multiple time series. These missing blocks do not only affect real-time monitoring but also compromise the quality of online data analyses. Effective streaming recovery (imputation) techniques either have a quadratic runtime complexity, which is infeasible for any moderately sized data, or cannot recover more than one time series at a time. In this paper, we introduce a new online recovery technique to recover multiple time series streams in linear time. Our recovery technique implements a novel incremental version of the Centroid Decomposition technique and reduces its complexity from quadratic to linear. Using this incremental technique, missing blocks are efficiently recovered in a continuous manner based on previous recoveries. We formally prove the correctness of our new incremental computation, which yields an accurate recovery. Our experimental results on real-world time series show that our recovery technique is, on average, 30% more accurate than the state of the art while being vastly more efficient. application/pdf https://folia.unifr.ch/global/documents/309158 https://folia.unifr.ch/documents/309158/files/2020_Cudre-Mauroux_ORBITS.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.14778/3430915.3430920 info:eu-repo/semantics/openAccess License undefined Proceedings of the VLDB Endowment. - 2020, vol. 14, no. 3, p. 294-306 Times Series Imputation info:eu-repo/classification/udc/004 ORBITS : Online Recovery of Missing Values in Multiple Time Series Streams http://purl.org/coar/resource_type/c_6501