Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks
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 exc...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2016/6709352 |
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| _version_ | 1832554225497800704 |
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| author | Narayanan Manikandan Srinivasan Subha |
| author_facet | Narayanan Manikandan Srinivasan Subha |
| author_sort | Narayanan Manikandan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-79614d8ea81a4a7ba944c5096b7c7e58 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-79614d8ea81a4a7ba944c5096b7c7e582025-02-03T05:52:07ZengWileyThe Scientific World Journal2356-61401537-744X2016-01-01201610.1155/2016/67093526709352Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural NetworksNarayanan Manikandan0Srinivasan Subha1School of Information Technology & Engineering, VIT University, Vellore, Tamil Nadu 632014, IndiaSchool of Information Technology & Engineering, VIT University, Vellore, Tamil Nadu 632014, IndiaSoftware 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.http://dx.doi.org/10.1155/2016/6709352 |
| spellingShingle | Narayanan Manikandan Srinivasan Subha Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks The Scientific World Journal |
| title | Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks |
| title_full | Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks |
| title_fullStr | Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks |
| title_full_unstemmed | Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks |
| title_short | Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks |
| title_sort | software design challenges in time series prediction systems using parallel implementation of artificial neural networks |
| url | http://dx.doi.org/10.1155/2016/6709352 |
| work_keys_str_mv | AT narayananmanikandan softwaredesignchallengesintimeseriespredictionsystemsusingparallelimplementationofartificialneuralnetworks AT srinivasansubha softwaredesignchallengesintimeseriespredictionsystemsusingparallelimplementationofartificialneuralnetworks |