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The Scientific World Journal
Volume 2016, Article ID 6709352, 10 pages
Review Article

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

School of Information Technology & Engineering, VIT University, Vellore, Tamil Nadu 632014, India

Received 1 October 2015; Accepted 29 November 2015

Academic Editor: Muthu Ramachandran

Copyright © 2016 Narayanan Manikandan and Srinivasan Subha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.