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Enhancing Financial Time Series Predictions with a Hybrid BNN-LSTM Approach

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Application of Back-Propagation Neural Network in Horoscope Prediction

by Usha Sharma, Sanjeev Karmakar, Navita Shrivastava
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 11 - Number 2
Year of Publication: 2016
Authors: Usha Sharma, Sanjeev Karmakar, Navita Shrivastava
10.5120/ijais2016451575

Usha Sharma, Sanjeev Karmakar, Navita Shrivastava . Application of Back-Propagation Neural Network in Horoscope Prediction. International Journal of Applied Information Systems. 11, 2 ( Jul 2016), 8-15. DOI=10.5120/ijais2016451575

@article{ 10.5120/ijais2016451575,
author = { Usha Sharma, Sanjeev Karmakar, Navita Shrivastava },
title = { Application of Back-Propagation Neural Network in Horoscope Prediction },
journal = { International Journal of Applied Information Systems },
issue_date = { Jul 2016 },
volume = { 11 },
number = { 2 },
month = { Jul },
year = { 2016 },
issn = { 2249-0868 },
pages = { 8-15 },
numpages = {9},
url = { https://www.ijais.org/archives/volume11/number2/906-2016451575/ },
doi = { 10.5120/ijais2016451575 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:03:46.876506+05:30
%A Usha Sharma
%A Sanjeev Karmakar
%A Navita Shrivastava
%T Application of Back-Propagation Neural Network in Horoscope Prediction
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 11
%N 2
%P 8-15
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this study a back-propagation neural network model is designed and its parameters are optimized for prediction of horoscope to identify a person type. Person type is a dynamic system based on the planet system. It is found that the back-propagation neural network is capable to predict the person type by learning planet dataset. The model is trained up to model error (i.e., mean square error) 1.2864E-04 and performs excellent during training and testing process.

References
  1. Tarsauliya, A., Kant, S., Kala, R., Tiwari, R., Shukla, A., 2010, “Analysis of Artificial Neural Network for Financial Time Series Forecasting”, International Journal of Computer Applications, 9 (5), 24-30.
  2. Peralta, J., Xiaodong Li, Gutierrez, G., and Sanchis, A., Time series forecasting by evolving artificial neural networks using genetic algorithms and differential evolution, IEEE World Congress on Computational Intelligence July, 18-23, 2010. CCIB, Barcelona, Spain.
  3. Akintola, K.G., Alese ,B.K, Thomson, A.F.,2011, Time Series forecasting with neural network, IJRRAS, 9(3),
  4. Juan José Montaño Moreno, Alfonso Palmer Pol and Pilar Muñoz Gracia, 2011, Artificial neural networks applied to forecasting time series, Psicothema, 23, (2), pp. 322-329.
  5. Khan, Z, H., Alin, T.S., Hussain, A., 2011, Price Prediction of Share Market using Artificial Neural Network (ANN), J. Computer 22(2).
  6. Vrabe,M., Mankovaa, I., Benoa, J., Tuharskýa,J., 2012 Surface roughness prediction using artificial neural networks when drilling Udimet 720, Procedia Engineering 48 693 – 700.
  7. Devi, J. , Reddy, B.S.P., Kumar, K.V., Reddy, B.M., Nayak, N.R., 2012, ANN Approach for Weather Prediction using Back Propagation, J. Engineering Trends and Technology- 3(1).
  8. Donate, J.P., Li,X., 2013, Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm, Neural Comput & Applic 22 pp.,11–20.
  9. Sa, Y., and Bouroumi, A., Prediction of Forest Fires Using Artificial Neural Networks, 2013, Applied Mathematical Sciences, 7(6), 271 – 286
  10. Vieira, C. 2013 Forecasting Financial Marketing , Thesis, School of echo, and management, Lisbon,.
  11. Tamizharasi, G. Kathiresan, S. Sreenivasan, K.S., 2014, Energy Forecasting using Artificial Neural Networks, J. Advanced Research in Electrical, Electronics and Instrumentation Engineering 3(3).
  12. Malik, P., Singh, S., Arora, B., 2014 An Effective Weather Forecasting Using Neural Network, J. Emerging Engineering Research and Technology 2(2), p. 209-212.
  13. Patel, M.B. , Yalamalle, S.R , 2014, Stock Price Prediction Using Artificial Neural Network J. Innovative Research in Science, Engineering and Technology, 3(6).
  14. Rao, A.N., Rao, K.E. , 2014, Estimize Bull speed using Back propagation J. Modern Engineering Research (IJMER), 4(11).
  15. Kuna, K, Time Series Prediction Using Neural Networks , Masaryk Univ., Faculty of Informatics, Thesis, Brno, Spring 2015.
  16. Enyindah, P. Uzochukwu, C , 2016, A Neural Network Approach to Financial Forecasting , J. Computer Applications 135(8),
  17. Shah, V.V., Mirani, S.J. Nanavati, Y.V., Narayanan, V., Pereira. S.I., Stock Market Prediction using NeuralNetworks, 2016, J. Soft Computing and Engineering (IJSCE) 6 (1).
  18. Karmakar.S.,Shrivastava.G., and Kowar.M.K., 2014, Impact of Learning Rate and Momentum Factor in the Performance of Back-Propagation Neural Network to Identify Internal Dynamics of Chaotic Motion, Kuwait J.Sci,41(2),151-174
Index Terms

Computer Science
Information Sciences

Keywords

Neural Network Prediction Back-propagation Horoscope

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