Fast Time Sequence Indexing for Arbitrary Lp Norms

By Byoung-Kee Yi, Christos Faloutsos. Very Large Data Bases Conference, 2000

774 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/45fbc5bda611197342c9c7dbee35782158e7395c

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Citations in the corpus

Warping indexes with envelope transforms for query by humming (2003, methodology): “In addition to DFT [1, 25, 35], Discrete Wavelet Transform (DWT) [6, 30, 23], Singular Value Decomposition (SVD)[16], Piecewise Aggregate Approximation (PAA)[32, 13] and Adaptive Piecewise Constant Approximation [14] approaches have also been proposed for similarity searching.”
On The Marriage of Lp-norms and Edit Distance (2004, methodology): “As shown in [29], the following corollary can be established for Lp-norms which are convex functions on reals.”
Querying and mining of time series data: experimental comparison of representations and distance measures (2008, background): “First, we compare the different variances of Lp-norms.”
Probabilistic discovery of time series motifs (2003, background): “There exists a vast body of work on efficiently locating known patterns in time series [1, 6, 12, 23, 35, 36, 37].”
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration (2002, background): “, 2000 , Yi and Faloutsos , 2000 ) .”
A review on time series data mining (2011, methodology): “That is, using the segmented means to represent the time series (Yi and Faloutsos, 2000).”
Time-series data mining (2012, methodology): “Numerous authors have pointed out several pitfalls when using Lp norms [Ding et al. 2008; Keogh and Kasetty 2003; Yi and Faloutsos 2000].”
Data-Driven Tree Transforms and Metrics (2017, methodology): “Also, note that interpreting the metric as the l1 distance in the averaging transform space enables us to apply approximate nearest-neighbor search algorithms suitable for the l1 distance [35], [36].”
Exact indexing of dynamic time warping (2002, methodology): “2001), and a simple technique independently introduced by two authors called piecewise aggregate approximation (PAA) (Keogh et al. 2000; Yi and Faloutsos 2000).”
TSclust: An R Package for Time Series Clustering (2014, methodology): “The SAX approach ﬁrst transforms the original data into the piecewise aggregate approximation (PAA) representation (Yi and Faloutsos 2000; Keogh, Chakrabarti, Pazzani, and Mehrotra 2000) and then symbolizes the PAA representation into a discrete string.”
Robust and fast similarity search for moving object trajectories (2005, background): “Most of the earlier proposals on similarity-based time series data retrieval are focused on one-dimensional time series data [1, 24, 23, 20, 40].”
The remote environmental assessment laboratory's acoustic library: An archive for studying soundscape ecology (2012, methodology): “Piecewise aggregate approximation (PAA) was introduced by Keogh et al. (2000), and independently by Yi and Faloutsos (2000), as a means to reduce the dimensionality of time series.”
Experiencing SAX: a novel symbolic representation of time series (2007, methodology): “…on the classic data mining tasks of clustering (Kalpakis et al. 2001), classiﬁcation (Geurts 2001), indexing (Agrawal et al. 1995; Faloutsos et al. 1994; Keogh et al. 2001a; Yi and Faloutsos 2000), and anomaly detection (Dasgupta and Forrest 1999; Keogh et al. 2002; Shahabi et al. 2000).”
Finding Motifs in Time Series (2002, methodology): “4 except for the fact that the distance between the two PAA coefficients has been replaced with the subfunction dist().”
Similarity search over time-series data using wavelets (2002, methodology): “[the] US Dollar have changed similarly to the price of gold for a specific period of time” [ 35 ].”
Discovering similar multidimensional trajectories (2002, methodology): “Various approaches have used, extended and indexed this distance metric [2, 37, 18, 14, 10, 32, 10, 20, 24, 23].”
StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time (2002, methodology): “Other techniques such as Discrete Wavelet Transform (DWT) [5, 26, 22, 12], Singular Value Decomposition (SVD)[18] and Piecewise Constant Approximation (PCA)[27, 17] are also proposed for similarity search.”
Locally adaptive dimensionality reduction for indexing large time series databases (2001, methodology): “(5) PAA (also known as Segmented Means [52]): The N-dimensional representation is computed by dividing the time series (of length n) into N-equal length segments and recording the means of those segments as proposed in [24] and [52].”