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Reseach Article

Trend based Approach for Time Series Representation

by Sagar S. Badhiye, Kalyani S. Hatwar, P. N. Chatur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 16
Year of Publication: 2015
Authors: Sagar S. Badhiye, Kalyani S. Hatwar, P. N. Chatur
10.5120/19909-1991

Sagar S. Badhiye, Kalyani S. Hatwar, P. N. Chatur . Trend based Approach for Time Series Representation. International Journal of Computer Applications. 113, 16 ( March 2015), 10-13. DOI=10.5120/19909-1991

@article{ 10.5120/19909-1991,
author = { Sagar S. Badhiye, Kalyani S. Hatwar, P. N. Chatur },
title = { Trend based Approach for Time Series Representation },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 16 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 10-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number16/19909-1991/ },
doi = { 10.5120/19909-1991 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:51:05.687072+05:30
%A Sagar S. Badhiye
%A Kalyani S. Hatwar
%A P. N. Chatur
%T Trend based Approach for Time Series Representation
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 16
%P 10-13
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Time series representation is one of key issues in time series data mining. Time series is simply a sequence of number collected at regular interval over a period of time and obtained from scientific and financial applications. The nature of time series data shows characteristics like large data size, high dimensional and necessity to update continuously. With the help of suitable choice of representation it will address high dimensionality issues and improve the efficiency of time series data mining. Symbolic Piecewise Trend Approximation is proposed to improve efficiency of time series data mining in high dimensional large databases. SPTA represents time series in trends form and obtained its values. Sign of value indicate changing direction and magnitude indicates degree of local trend. Depending on the trend of time series, it is segmented into samples of different size which are approximated by the ratio between first and last points within the segment. Each segment then represented by alphabet. The time series is thus represented as sequence of alphabets thus reducing its dimension. Validate SPTA with naïve based classification method.

References
  1. Tak-chung Fu, "A review on time series data mining", Engneering Application of Artificial Intelligence: Elsevier Publication, Sept 2010.
  2. C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, "Fast subsequence matching in time-series databases," in SIGMOD Conference, 1994, pp. 419–429.
  3. D. J. Berndt and J. Clifford, "Using Dynamic Time Warping to Find Patterns in Time Series," in KDD Workshop, 1994, pp. 359–370.
  4. Jingpei Dan,1 Weiren Shi,2 Fangyan Dong,3 and Kaoru Hirota3, "Piecewise Trend Approximation: A Ratio-Based Time Series Representation", in Hindawi Publishing Corporation Abstract and Applied Analysis Volume 2013, Article ID 603629.
  5. Hailin Li, Chonghui Guo, "Piecewise cloud approximation for time series mining", Knowledge-Based Systems: Elsevier Publication, Dec 2010.
  6. Lei Sun, Yujiu Yang, Wenhuang Liu, "Trended DTW Based On Piecewise Linear Approximation for Time Series Mining", in 11th IEEE International Conference on Data Mining Workshops, 2011.
  7. Tao Sun, Hongfeng Sun, Weiheng Chen, "Dimensionality Reduction for Interval Time Series", IEEE World Congress on Information and Communication ,2012
  8. Peiman barnaghi, A. Abu Bakar, Z. Ali Othman "Enhanced symbolic Aggregate approximation method for financial time series data representation" Data Mining and Knowledge Discovery , 2007.
  9. A. Notaristefano, G. Chicco, F. piglione "Data size reduction with symbolic aggregate" in IET Generation, Transmission and Distribution, July 2012.
  10. Y. Ding, X. Yang, A. J. Kavs, and J. Li, "A novel piecewise linear segmentation for time series," in Proceedings of the 2nd International Conference on Computer and Automation Engineering, February, 2010.
  11. Chung, F. L. , Fu, T. C. , Luk, R. , Ng, V. , "Flexible time series pattern matching based on perceptually important points" , in International Joint Conference on Artificial Intelligence Workshop on Learning from Temporal and Spatial Data, pp. 1–7, 2001.
  12. http://www. nseindia. com/live_market/dynaContent/live_watch/equities_stock_watch. htm?cat=N
Index Terms

Computer Science
Information Sciences

Keywords

Data mining Time series representation Time series piecewise trends Approximation