rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: High performance discovery in time series
    sub title: techniques and case studies
  name:
    name part: Shasha, Dennis Elliott.
    role:
      role term: creator
  name:
    name part: Zhu, Yunyue.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: nyu
    date issued: 2004
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (ix, 190 pages) : illustrations.
  abstract: Time-series datadata arriving in time order, or a data streamcan be found in fields such as physics, finance, music, networking, and medical instrumentation. Designing fast, scalable algorithms for analyzing single or multiple time series can lead to scientific discoveries, medical diagnoses, and perhaps profits. High Performance Discovery in Time Series presents rapid-discovery techniques for finding portions of time series with many events (i.e., gamma-ray scatterings) and finding closely related time series (i.e., highly correlated price and return histories, or musical melodies). A typical time-series technique may compute a "consensus" time seriesfrom a collection of time seriesto use regression analysis for predicting future time points. By contrast, this book aims at efficient discovery in time series, rather than prediction, and its novelty lies in its algorithmic contributions and its simple, practical algorithms and case studies. It presumes familiarity with only basic calculus and some linear algebra. Topics and Features: *Presents efficient algorithms for discovering unusual bursts of activity in large time-series databases * Describes the mathematics and algorithms for finding correlation relationships between thousands or millions of time series across fixed or moving windows *Demonstrates strong, relevant applications built on a solid scientific basis *Outlines how readers can adapt the techniques for their own needs and goals *Describes algorithms for query by humming, gamma-ray burst detection, pairs trading, and density detection *Offers self-contained descriptions of wavelets, fast Fourier transforms, and sketches as they apply to time-series analysis This new monograph provides a technical survey of concepts and techniques for describing and analyzing large-scale time-series data streams. It offers essential coverage of the topic for computer scientists, physicists, medical researchers, financial mathematicians, musicologists, and researchers and…
  table of contents: Part I. Review of techniques -- 1. Time series preliminaries -- 2. Data reduction and transformation techniques -- 3. Indexing methods -- 4. Flexible similarity search -- Part II. Case studies -- 5. StatStream -- 6. Query by humming -- 7. Elastic burst detection -- 8. A call to exploration.
  note: Dennis Shasha, Yunyue Zhu.
  note: Includes bibliographical references (pages [181]-187) and index.
  subject:
    topic: Time-series analysis
  subject:
    topic: Tijdreeksen
  subject:
    topic: Algorithmus
  subject:
    topic: Zeitreihenanalyse
  subject:
    topic: Time-series analysis
  classification: QA280 .S53 2004eb
  classification: 519.5/5
  classification: 31.73
  classification: 510
  classification: QH 237
  classification: ST 130
  related item:
    title info:
      title: High performance discovery in time series
    name:
      name part: Shasha, Dennis Elliott.
    identifier: (DLC) 2004556616
    identifier: (OCoLC)56035818
  related item:
    title info:
      title: Monographs in computer science
  identifier: 9781475740462 (electronic bk.)
  identifier: 1475740468 (electronic bk.)
  identifier: 0387008578
  identifier: 9780387008578
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: GW5XE
    record creation date: 140711
    record change date: 20260420205516.5
    record identifier: in00003549835
    language of cataloging:
      language term: eng