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Type Journal Article
Scope Discipline-based scholarship
Title Scalable recovery of missing blocks in time series with high and low cross-correlations
Organization Unit
Authors
  • Mourad Khayati
  • Philippe Cudré-Mauroux
  • Michael Hanspeter Böhlen
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Knowledge and Information Systems (KAIS)
Publisher Springer
Geographical Reach international
ISSN 0219-3116
Volume 62
Number 6
Page Range 2257 - 2280
Date 2020
Abstract Text Missing values are very common in real-world data including time-series data. Failures in power, communication or storage can leave occasional blocks of data missing in multiple series, affecting not only real-time monitoring but also compromising the quality of data analysis. Traditional recovery (imputation) techniques often leverage the correlation across time series to recover missing blocks in multiple series. These recovery techniques, however, assume high correlation and fall short in recovering missing blocks when the series exhibit variations in correlation. In this paper, we introduce a novel approach called CDRec to recover large missing blocks in time series with high and low correlations. CDRec relies on the centroid decomposition (CD) technique to recover multiple time series at a time. We also propose and analyze a new algorithm called Incremental Scalable Sign Vector to efficiently compute CD in long time series. We empirically evaluate the accuracy and the efficiency of our recovery technique on several real-world datasets that represent a broad range of applications. The results show that our recovery is orders of magnitude faster than the most accurate algorithm while producing superior results in terms of recovery.
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Digital Object Identifier 10.1007/s10115-019-01421-7
Other Identification Number merlin-id:20735
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