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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
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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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