A method for detecting changes in long time series

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Description

Modern scientific activities, both physical and computational, can result in time series of many thousands or even millions of data values. Here the authors describe a statistically motivated algorithm for quick screening of very long time series data for the presence of potentially interesting but arbitrary changes. The basic data model is a stationary Gaussian stochastic process, and the approach to detecting a change is the comparison of two predictions of the series at a time point or contiguous collection of time points. One prediction is a ``forecast``, i.e. based on data from earlier times, while the other a ``backcast``, ... continued below

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20 p.

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Downing, D.J.; Lawkins, W.F.; Morris, M.D. & Ostrouchov, G. September 1, 1995.

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Description

Modern scientific activities, both physical and computational, can result in time series of many thousands or even millions of data values. Here the authors describe a statistically motivated algorithm for quick screening of very long time series data for the presence of potentially interesting but arbitrary changes. The basic data model is a stationary Gaussian stochastic process, and the approach to detecting a change is the comparison of two predictions of the series at a time point or contiguous collection of time points. One prediction is a ``forecast``, i.e. based on data from earlier times, while the other a ``backcast``, i.e. based on data from later times. The statistic is the absolute value of the log-likelihood ratio for these two predictions, evaluated at the observed data. A conservative procedure is suggested for specifying critical values for the statistic under the null hypothesis of ``no change``.

Physical Description

20 p.

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OSTI as DE96002374

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  • Other Information: PBD: Sep 1995

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  • Other: DE96002374
  • Report No.: ORNL/TM--12879
  • Grant Number: AC05-84OR21400
  • DOI: 10.2172/161513 | External Link
  • Office of Scientific & Technical Information Report Number: 161513
  • Archival Resource Key: ark:/67531/metadc623438

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Creation Date

  • September 1, 1995

Added to The UNT Digital Library

  • June 16, 2015, 7:43 a.m.

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  • Jan. 19, 2016, 7:48 p.m.

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Downing, D.J.; Lawkins, W.F.; Morris, M.D. & Ostrouchov, G. A method for detecting changes in long time series, report, September 1, 1995; Tennessee. (digital.library.unt.edu/ark:/67531/metadc623438/: accessed August 14, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.