3 edition of High performance discovery in time series found in the catalog.
High performance discovery in time series
|Statement||Dennis Shasha, Yunyue Zhu.|
|The Physical Object|
|Pagination||xiii, 190 p. :|
|Number of Pages||190|
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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 series 5/5(2). A typical time-series technique may compute a "consensus" time series—from a collection of time series—to 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. High Performance Discovery In Time Series: Techniques And Case Studies (Monographs in Computer Science) June High Performance Discovery in Time Series by Dennis Elliott Shasha, Yunyue Zhu and a great selection of related books, art and collectibles available now at - High Performance Discovery in Time Series: Techniques and Case Studies Monographs in Computer Science - AbeBooks.
Find helpful customer reviews and review ratings for High Performance Discovery High performance discovery in time series book Time Series: Techniques And Case Studies (Monographs In Computer Science) at Read honest and unbiased product reviews from our users.5/5. Find many great new & used options and get the best deals for Monographs in Computer Science: High Performance Discovery in Time Series: Techniques and Case Studies by New York University Staff (, Paperback) at the best online prices at eBay.
Free shipping for many products. Get this from a library. High performance discovery in time series: techniques and case studies. [Dennis Elliott Shasha; Yunyue Zhu] -- "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. This monograph is a technical survey of concepts and techniques for describing and analyzing large-scale time-series data streams. Some topics covered are algorithms for query by humming, gamma-ray burst detection, pairs trading, and density detection.
Included are self-contained descriptions Price: $ Read High Performance Discovery In Time Series Techniques and Case Studies Monographs in Ebook Online. High Performance Discovery In Time Series 作者: New York University 出版社: Springer 副标题: Techniques and Case Studies 出版年: 页数: 定价: GBP 装帧:.
High Performance Data Mining in Time Series: Techniques and Case Studies by Yunyue Zhu A dissertation submitted in partial fulﬂllment of the requirements for the degree of Doctor of Philosophy Department of Computer Science New York University January Dennis Shasha °File Size: 1MB. High Performance Discovery In Time Series: Techniques And Case Studies by Dennis Shasha, Yunyue Zhu.
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Since the synthetic method often dictates the appropriate High performance discovery in time series book of analysis, each chapter. We apply wavelet analysis to high-frequency time series data from various sources: (1) the interplanetary magnetic field (from the ACE satellite), (2) the ionospheric parameters – TEC (from ionospheric sounding stations), and (3) the ground geomagnetic data (from ground geomagnetic observatories, located at middle geographic latitudes).
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I think the mainstay textbook on this (for economists anyway) is James Hamilton's Time Series Analysis . If this is your passion, do get it. However, it's long and very dry and for a first-timer, not great to read at all.
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Cost Efficiency. Most time series models have little forecasting power. They just spit out either 1) the last value, 2) the mean of the time points corresponding to the historic periodicity. $\endgroup$ – horaceT May.
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