Summarizing a set of time series by averaging: from Steiner sequence to compact multiple alignment

DOI10.1016/j.tcs.2011.09.029zbMath1232.68105OpenAlexW2068982516WikidataQ56228140 ScholiaQ56228140MaRDI QIDQ764307

François Petitjean, Pierre Gançarski

Publication date: 13 March 2012

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2011.09.029

zbMATH Keywords

soft computingdynamic time warpingmultiple alignmenttime series averagingcompact multiple alignmentconsensus sequencetime series summarizing

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Algorithms on strings (68W32)

Related Items (6)

Approximating ( k,ℓ )-Median Clustering for Polygonal Curves ⋮ Tight Hardness Results for Consensus Problems on Circular Strings and Time Series ⋮ Exact mean computation in dynamic time warping spaces ⋮ SimUSF: an efficient and effective similarity measure that is invariant to violations of the interval scale assumption ⋮ Consensus patterns of a set of time series via a wavelet-based temporal localization: emphasizing the utility over point-wise averaging and averaging under dynamic time warping ⋮ Times series averaging and denoising from a probabilistic perspective on time-elastic kernels

Uses Software

• MUSCLE
• Kalign
• T-coffee
• PROMALS
• PCMA

Cites Work

• A global averaging method for dynamic time warping, with applications to clustering
• Dynamic programming algorithm optimization for spoken word recognition
• Algorithms on Strings, Trees and Sequences
• Solving the Graphical Steiner Tree Problem Using Genetic Algorithms
• An adaptive genetic clustering method for exploratory mining of feature vector and time series data
• Steiner Minimal Trees
• Unnamed Item
• Unnamed Item
• Unnamed Item

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