ORBITS : Online Recovery of Missing Values in Multiple Time Series Streams
- Khayati, Mourad University of Fribourg
- Arous, Ines University of Fribourg
- Tymchenko, Zakhar University of Fribourg
- Cudré-Mauroux, Philippe University of Fribourg
- 01.11.2020
Published in:
- Proceedings of the VLDB Endowment. - 2020, vol. 14, no. 3, p. 294-306
English
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.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département d'informatique
- Language
-
- English
- Classification
- Computer science and technology
- License
- License undefined
- Identifiers
-
- RERO DOC 330488
- DOI 10.14778/3430915.3430920
- Persistent URL
- https://folia.unifr.ch/unifr/documents/309158
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