Landmarks: a new model for similarity-based pattern querying in time series databases

By Chang-Shing Perng, Haixun Wang, Sylvia R. Zhang, D. S. Parker. Proceedings / International Conference on Data Engineering, 2000

317 citations recorded by the source corpus.

Subfields: Signal Processing

View Paper: https://www.semanticscholar.org/paper/efa9473b74de6c9897ddf5b8245dd90921815b6d

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In Signal Processing

Citations in the corpus

Finding Motifs in Time Series (2002, background): “A task associated with subsequences is to determine if a given subsequence is similar to other subsequences [1, 2, 3, 8, 13, 19, 21, 22, 23, 24, 25, 27, 29, 35, 40].”
Discovering similar multidimensional trajectories (2002, methodology): “Other techniques to define time series similarity are based on extracting certain features (Landmarks [29] or signatures [13]) from each time-series and then use these features to define the similarity.”
A Feature Fusion Based Forecasting Model for Financial Time Series (2014, methodology): “Plenty of methods are proposed to remove noise and reduce the dimensions of raw data, such as Principal Component Analysis (PCA) [2], Kernel Principal Component Analysis [3], Perpetually Important Points [4], Piecewise Aggregate Approximation[5], Singular Spectral Analysis[6], Discrete Fourier Trans”
A review on time series data mining (2011, methodology): “Although most of these approaches guarantee that a lower bound of the Euclidean distance to the original data, Euclidean distance is not always being the suitable distance function in specified domains (Keogh, 1997a; Perng et al., 2000; Megalooikonomou et al., 2005).”
An online algorithm for segmenting time series (2001, background): “Several high level representations of time series have been proposed, including Fourier Transforms [1] [13], Wavelets [4], Symbolic Mappings [2] [5] [24] and Piecewise Linear Representation (PLR).”
A survey of temporal data mining (2006, background): “Perng et al (2000) propose similarity measures which are invariant under various transformations (like shifting, amplitude scaling etc.).”
Pattern Matching in Historical Data (2002, methodology): “However, the methodologies of Smyth 1999 Ž .and Perng et al. 2000 are restricted to univariate time-series data.”
Segmenting Time Series: A Survey and Novel Approach (2002, unknown): “Several high level representations of time series have been proposed, including Fourier Transforms [1,13], Wavelets [4], Symbolic Mappings [2, 5, 24 ] and Piecewise Linear Representation (PLR).”
Locally adaptive dimensionality reduction for indexing large time series databases (2001, background): “The work of [3, 36, 45, 25, 26] differs from the above in that they focus in providing a more flexible query language and not on performance issues.”
Time-series data mining (2012, background): “The Landmarks system [Perng et al. 2000] extends this notion to include a multiresolution property.”
Series distance – an intuitive metric to quantify hydrograph similarity in terms of occurrence, amplitude and timing of hydrological events (2011, unknown): “This is in our eyes superior to the approach proposed by Perng et al. (2000), who uses patterns of single characteristic landmarks such as peaks or troughs for time series comparison.”