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Parallel Algorithm for Time Series Based Forecasting on OTIS-Mesh

by Sudhanshu Kumar Jha, Prasanta K. Jana
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 26
Year of Publication: 2010
Authors: Sudhanshu Kumar Jha, Prasanta K. Jana
10.5120/477-784

Sudhanshu Kumar Jha, Prasanta K. Jana . Parallel Algorithm for Time Series Based Forecasting on OTIS-Mesh. International Journal of Computer Applications. 1, 26 ( February 2010), 70-75. DOI=10.5120/477-784

@article{ 10.5120/477-784,
author = { Sudhanshu Kumar Jha, Prasanta K. Jana },
title = { Parallel Algorithm for Time Series Based Forecasting on OTIS-Mesh },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 26 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 70-75 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number26/477-784/ },
doi = { 10.5120/477-784 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:48:52.676275+05:30
%A Sudhanshu Kumar Jha
%A Prasanta K. Jana
%T Parallel Algorithm for Time Series Based Forecasting on OTIS-Mesh
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 26
%P 70-75
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Forecasting plays an important role in business, technology, climate and many others. As an example, effective forecasting can enable an organization to reduce lost sales, increase profits and more efficient production planning. In this paper, we present a parallel algorithm for short term forecasting based on a time series model called weighted moving average. Our algorithm is mapped on OTIS-mesh, a popular model of optoelectronic parallel computers.. Scalability of the algorithm is also discussed.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Parallel algorithm OTIS-mesh time series forecasting weighted moving average

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