Distortion-free predictive streaming time-series matching

Loh, Woong-Kee; Moon, Yang-Sae; Srivastava, Jaideep

doi:10.1016/j.ins.2009.12.009

Cited by 15 publications

(8 citation statements)

References 47 publications

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“…In similar sequence matching, there have been many efforts to remove these distortions from time-series data. For example, offset translation and amplitude scaling can be solved by the normalization transform, and its subsequence matching solutions were proposed in [11,15,12]. Also, the moving average transform can alleviate noise of time-series, and its subsequence matching solution was proposed in [14].…”

Section: Related Workmentioning

confidence: 99%

“…Linear trend, a representative distortion of time-series data [9,12], shows the directionality of a time-series, and linear detrending in similar sequence matching is crucial to get the more intuitive matching results. Figure 1 shows an example of comparing two sequences before and after linear detrending: Figure 1(a) represents the original sequences Q and S; Figure 1(b) the linear detrended sequences Q ′ and S ′ .…”

Section: Introductionmentioning

confidence: 99%

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Linear Detrending Subsequence Matching in Time-Series Databases

Gil

Moon

Kim

2011

IEICE Trans. Inf. & Syst.

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SUMMARYEvery time-series has its own linear trend, the directionality of a time-series, and removing the linear trend is crucial to get more intuitive matching results. Supporting the linear detrending in subsequence matching is a challenging problem due to the huge number of all possible subsequences. In this paper we define this problem as the linear detrending subsequence matching and propose its efficient index-based solution.To this end, we first present a notion of LD-windows (LD means linear detrending). Using the LD-windows we then present a lower bounding theorem for the index-based matching solution and show its correctness. We next propose the index building and subsequence matching algorithms. We finally show the superiority of the index-based solution.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Linear Detrending Subsequence Matching in Time-Series Databases

Gil

Moon

Kim

2011

IEICE Trans. Inf. & Syst.

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“…Finding data sequences similar to the given query sequence from the database is called time-series matching [2,3,14,16,21,32]. In this paper we perform time-series matching under the Euclidean distance-based similarity model [2,10,31].…”

Section: Time-series Matchingmentioning

confidence: 99%

Scaling-invariant boundary image matching using time-series matching techniques

Moon¹,

Kim²,

Kim³

et al. 2010

Data & Knowledge Engineering

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“…This method is an extension of the free-form deformation (FFD) technique. More recent research can be referred to [3,9,11,16,18,21,25,26,28,30,31].…”

Section: Previous Workmentioning

confidence: 99%