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Reseach Article

Time Series Representation for Identification of Extremes

by Rajesh Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 97 - Number 14
Year of Publication: 2014
Authors: Rajesh Kumar

Rajesh Kumar . Time Series Representation for Identification of Extremes. International Journal of Computer Applications. 97, 14 ( July 2014), 14-19. DOI=10.5120/17075-7515

@article{ 10.5120/17075-7515,
author = { Rajesh Kumar },
title = { Time Series Representation for Identification of Extremes },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 97 },
number = { 14 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { },
doi = { 10.5120/17075-7515 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:24:06.698007+05:30
%A Rajesh Kumar
%T Time Series Representation for Identification of Extremes
%J International Journal of Computer Applications
%@ 0975-8887
%V 97
%N 14
%P 14-19
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

Extracting information from the huge time series is a challenging task. Databases are prepared by keeping in mind the type of information required. Indexing a time series is a difficult task, where shape may not be exact. Finding the minima and maxima of the time series is another difficult job dependent on the expert's subjectivity. In this information age algorithmic trading is the buzz word. For the identification of various patterns, a machine can be made intelligent by embedding some good algorithm in the trading module to identify the various patterns. In this paper an attempt has been made to identify the extremes, where profit probability is maximized.

  1. R Agarwal, G . Psaila, E. l. Wimmers, M. Zait, 1995,Querying shapes of histories, in proc. Of the21st international conference on very large databases, pp 502,514.
  2. S. E Paraskevopoulou, D. Y Barsakcioglu, M. R Saberi, Amir Eftekhar, T. G. Constandinou, Dec 24, 2012,Feature Extraction using First and Second Derivative Extreme (FSDE) for Real-time and Hardware-Efficient Spike Sorting, Journal of neuroscience method pp 1-12
  3. Yang Z, Zhao Q, Liu W,2009, Improving spike separation using waveform derivatives. Journal of Neural Engineering 2009;6(4):2–12.
  4. H. Andre Jonsoon,2002,Indexing strategies for time series data, Linkoping studies in science and technology, diss. no 757,ISBN 91-7373-346-6.
  5. J. Lin,E. Keogh, S Lonardi, B. Chiu, june 13,2003,A symbolic representation of time series with implications of streaming algorithms. DMKD, San Diego C. A
  6. L. W. N. Kumar ,Venkata Loilla, E. Keogh, S. Lonardi ,C. Ann,Ratanamahatana, Assumption-Free Anomaly Detection in Time Series, partly funded by the National Science Foundation under grant IIS-0237918.
  7. J. lin,E. Keogh,S. Lonardi,J. P. Lankford,D. M Nystorm,2004, Visually Mining and Monitoring Massive Time Series, in proceeding of 10th ACM SIGKDD.
  8. Kumar, N. Lolla N. , Keogh, E. Lonardi, S. Ratanamahatana, C. & Wei, L. 2005. Time-series Bitmaps: A Practical Visualization Tool for Working with Large Time Series Databases. SIAM 2005 Data Mining Conference.
  9. R. Agarwal,C. Faloustos,A. Swami,Efficient similarity search in sequence databases,funded by national science foundation, grant no 895846.
  10. D. Abadiand ,Aurora, A data stream management system . In SIGMOD 2003.
  11. S. Guha, N. Kudos , Approximating a data stream for querying and estimation: algorithms and evaluation performance In ICDE 2002.
  12. F. Rasheed, M. Ashaalfa,R. Alhajj,2011, Efficient Periodicity Mining in Time Series Databases Using Suffix Trees, Ieee transactions on data engineering, vol. 23, no1, page 79-94
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