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Time Series Data Analysis For Long Term Forecasting and Scheduling of Organizational Resources – Few Cases

by Sunil Bhaskaran
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 12
Year of Publication: 2012
Authors: Sunil Bhaskaran
10.5120/5591-7200

Sunil Bhaskaran . Time Series Data Analysis For Long Term Forecasting and Scheduling of Organizational Resources – Few Cases. International Journal of Computer Applications. 41, 12 ( March 2012), 4-9. DOI=10.5120/5591-7200

@article{ 10.5120/5591-7200,
author = { Sunil Bhaskaran },
title = { Time Series Data Analysis For Long Term Forecasting and Scheduling of Organizational Resources – Few Cases },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 12 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 4-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number12/5591-7200/ },
doi = { 10.5120/5591-7200 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:29:23.344255+05:30
%A Sunil Bhaskaran
%T Time Series Data Analysis For Long Term Forecasting and Scheduling of Organizational Resources – Few Cases
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 12
%P 4-9
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Our society is increasingly influenced by modern information and communication technology (ICT), Data warehouse, data mining and time series data mining etc. Time series data mining can be treated as a subset of data mining domain. The nature of time series data is large data size, high dimensionality and necessary to update continuously. Time series data mining (TSDM) is a rapidly evolving research area in Computer Science. While processing data stored in a data base, if we consider the time at which the event happened, the information technology professional can generate more reliable and dependable information in comparison with conventional methods. Potentially, today, every stake holders has got the opportunity for time series data mining. In this paper I am introducing a methodology and a strategy for the effective planning of various organizational resources for different stake holders in the form of cases. Few of them are Information Technology professionals planning their hardware, software and network (bandwidth) requirement for the organizations. Another category of user is top level business executives, they are responsible for the long term strategic decision making of their business. The next category of users I am planing to cover in this paper is medical professionals or biological researchers and share traders (equity market).

References
  1. Sheng Chang, Wynne Hsu and Mong Li Lee . 2006. Mining Dense Periodic Patterns in Time Series Data, Proceedings of the 22nd International conference on data engineering (ICDE'06), 8-7695-2570-9/06, IEEE.
  2. Jose Zubcoff , Jesús Pardillo and Juan Trujillo . 2009. A UML profile for the conceptual modeling of data-mining with time-series in data warehouses. Information and Software Technology 51(2009) 977–992, Science Direct, ELSEVIER.
  3. Juan Trujillo. 2011. A review on time series data mining - Engineering Applications of Artificial Intelligence, 24 (2011) 164–181. ELSEVIER.
  4. Xiao Hu, Peng Xu, Shaozhi Wu, Shadnaz Asgari and Marvin Bergsneider . 2010. "A data mining framework for time series estimation. Journal of Biomedical Informatics" 43 (2010) 190–199. ELSEVIER.
  5. Das P. K, Maya Nayak , Senapati. M. R and Lee I. W. C. 2007. Mining for similarities in time series data using wavelet-based feature vectors and neural networks. Engineering Applications of Artificial Intelligence, 20 (2007) 185–201. Science Direct, ELSEVIER.
  6. Zhe Song, Xiulin Geng, Andrew Kusiak, and Chang Xu. 2011. Mining Markov chain transition matrix from wind speed time series data. Expert Systems with Applications xxx (2011) xxx–xxx. Science Direct, ELSEVIER.
  7. Chun-Hao Chen, Tzung-Pei Hong and Vincent S. Tseng . 2009. Mining fuzzy frequent trends from time series. Expert Systems with Applications, 36 (2009) 4147–4153. Science Direct, ELSEVIER.
  8. Huei-Wen Wu and Anthony J. T. Lee. 2009. Mining closed patterns in multi-sequence time-series databases. Data & Knowledge Engineering, 68 (2009) 1071–1090, Science Direct, ELSEVIER.
  9. Dash P. K, Behera H. S and Lee I. W. C 2009. Time sequence data mining using time–frequency analysis and soft computing techniques. Applied Soft Computing, 8 (2008) 202–215. Science Direct, ELSEVIER.
  10. Hailin Li and Chonghui Guo . 2011. Piecewise cloud approximation for time series mining. Knowledge-Based Systems, 24 (2011) 492–500. IEEE, Science Direct, ELSEVIER.
  11. W. N. Venables, D. M. Smith and the R Development Core Team , "An Introduction to R. Manual of R language" , Institute for Statistics and Mathematics, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Future And Option (f&o) Segment Day Trading Time Series Data Mining Swap Area Equity Market Filtering Smoothening

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