Distortion-free predictive streaming time-series matching

Woong Kee Loh, Yang Sae Moon, Jaideep Srivastava

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Efficient processing of streaming time-series generated by remote sensors and mobile devices has become an important research area. As in traditional time-series applications, similarity matching on streaming time-series is also an essential research issue. To obtain more accurate similarity search results in many time-series applications, preprocessing is performed on the time-series before they are compared. The preprocessing removes distortions such as offset translation, amplitude scaling, linear trends, and noise inherent in time-series. In this paper, we propose an algorithm for distortion-free predictive streaming time-series matching. Similarity matching on streaming time-series is saliently different from traditional time-series in that it is not feasible to directly apply the traditional algorithms for streaming time-series. Our algorithm is distortion-free in the sense that it performs preprocessing on streaming time-series to remove offset translation and amplitude scaling distortions at the same time. Our algorithm is also predictive, since it performs streaming time-series matching against the predicted most recent subsequences in the near future, and thus improves search performance. To the best of our knowledge, no streaming time-series matching algorithm currently performs preprocessing and predicts future search results simultaneously.

Original languageEnglish (US)
Pages (from-to)1458-1476
Number of pages19
JournalInformation Sciences
Volume180
Issue number8
DOIs
StatePublished - Apr 15 2010

Keywords

  • Multiple indexing
  • Normalization transform
  • Predictive matching
  • Search cost estimation
  • Streaming time-series matching

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