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Financial Time Series Volatility Forecast Using Evolutionary Hybrid Artificial Neural Network [chapter]

Anupam Tarsauliya, Rahul Kala, Ritu Tiwari, Anupam Shukla
2011 Communications in Computer and Information Science  
Statistical methods such as GARCH, GJR, EGARCH and Artificial Neural Networks (ANNs) based on standard learning algorithms such as backpropagation have been widely used for forecasting time series volatility  ...  Financial time series forecast has been classified as standard problem in forecasting due to its high non-linearity and high volatility in data.  ...  Supervised learning is followed for learning of the neural network with target prediction series given. Artificial neural network thus obtained is evolved using genetic algorithm.  ... 
doi:10.1007/978-3-642-22540-6_44 fatcat:wxiznlv555b7zoostltzt6cohe

ALEC: An Adaptive Learning Framework for Optimizing Artificial Neural Networks [chapter]

Ajith Abraham, Baikunth Nath
2001 Lecture Notes in Computer Science  
We explored the performance of ALEC and artificial neural networks for function approximation problems.  ...  To evaluate the comparative performance, we used three different well-known chaotic time series.  ...  Experimental Setup Using Artificial Neural Networks We used a feedforward network with 1 hidden layer and the training was performed for 2500 epochs.  ... 
doi:10.1007/3-540-45718-6_19 fatcat:ihv7gxsrinelviocz7qr6cnmp4

Aggregated Financial Forecasting Calculation in HumanComputer Distributed Computing

Petar Tomov, Gergana Mateeva, Dimitar Parvanov
2021 Problems of Engineering Cybernetics and Robotics  
The idea of a desktop computer screen-saver used for artificial neural networks training was extended in VitoshaTrade project where Android Active Wallpaper is used for the same purpose.  ...  Three of the most famous financial forecasting donated distributed computing projects for the last decade were MoneyBee, GStock and MQL5 Cloud Network.  ...  When artificial neural networks are used for financial time series forecasting, the training can be organized with genetic algorithms [13] or differential evolution [14] .  ... 
doi:10.7546/pecr.75.21.06 fatcat:uiwr6oozobbf7lad5rsl6ukmky

Wind speed forecasting: Present status

Melam Bhaskar, Amit Jain, N. Venkata Srinath
2010 2010 International Conference on Power System Technology  
In the recent years there is a lot of research happening to predict wind speed with several mathematical methods and biologically inspired computing techniques to reduce the prediction error.  ...  It is gaining more attention with the recent evolution of smart grid, which throws a challenge of integrating wind power into the grid.  ...  Artificial Neural Networks Artificial Neural Networks (ANN) have been a good selection to model and forecast time series.  ... 
doi:10.1109/powercon.2010.5666623 fatcat:6dpwwut22vc5fjxbjc6htm4kdm

A hybrid method for tuning neural network for time series forecasting

Aranildo Rodrigues Lima Junior, Tiago Alessandro Espínola Ferreira
2008 Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08  
The GRAPES tuning and evolve the Artificial Neural Network parameters configuration, the weights and the minimum number of (and their specific) relevant time lags, searching an optimal or sub-optimal forecasting  ...  This paper presents an study about a new Hybrid method -GRASPES -for time series prediction, inspired in F.  ...  automatically the weights, architecture and inputs (relevante lags) of the Artificial Neural Network (ANN) applied to time series forecasting problem [5, 6] .  ... 
doi:10.1145/1389095.1389197 dblp:conf/gecco/JuniorF08 fatcat:zqlqxq5ul5g6rbssqyatbdged4

Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning [article]

Arka Ghosh
2012 arXiv   pre-print
model with Feedforward Multilayer Artificial Neural Network & Recurrent Time Delay Neural Network for the Financial Timeseries Prediction.This study is developed with the help of historical stockprice  ...  dataset made available by GoogleFinance.To develop this prediction model Backpropagation method with Gradient Descent learning has been implemented.Finally the Neural Net, learned with said algorithm  ...  IV.CONCLUSION This paper presented a hybrid neural-evolutionary methodology to forecast time-series.  ... 
arXiv:1111.4930v2 fatcat:gbidepcc7jcz5jzsinihygyf6m

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

Narayanan Manikandan, Srinivasan Subha
2016 The Scientific World Journal  
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  ...  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  ...  Parallel Artificial Neural Networks (PANN) in prediction and modelling are used.  ... 
doi:10.1155/2016/6709352 pmid:26881271 pmcid:PMC4735934 fatcat:rckeurpilvh2znyz7olr4tuile

Soft Computing Techniques in combating the complexity of the atmosphere- a review [article]

Surajit Chattopadhyay
2006 arXiv   pre-print
The purpose of the present review is to discuss the role of Soft Computing techniques in understanding the complexity associated with atmospheric phenomena and thus developing predictive models.  ...  Logic and Artificial Neural Network in predicting chaotic time series.  ...  Artificial Neural Net can predict chaotic atmospheric time series and can recognize various complicated patterns intrinsic in the time series.  ... 
arXiv:nlin/0608052v1 fatcat:jzyzqf7xajdava527ri3g473ii

PEPNet: Parallel evolutionary programming for constructing artificial neural networks [chapter]

Gerrit A. Riessen, Graham J. Williams, Xin Yao
1997 Lecture Notes in Computer Science  
This paper presents a description of an evolutionary artificial neural network algorithm, EP-Net and its extension taking advantage of a High Performance Computing Environment.  ...  Experimental studies have shown promising results with better time and prediction performance.  ...  Parallel Evolutionary Artificial Neural Networks (PEANNs) have the potential to produce accurate networks in significantly less time using larger datasets than serial EANNs.  ... 
doi:10.1007/bfb0014799 fatcat:g3c37lzlqbbwxkje6446howcaq

Preface [chapter]

2020 Soft Computing  
Needless to say, soft computing techniques are an emerging approach, which includes techniques such as fuzzy logic, evolutionary computing, artificial neural network, and applied statistics.  ...  Moreover, they are easy to accommodate with changed scenario and can be executed with parallel computing.  ...  Acknowledgments It is well known that besides the editors, many individuals have put much time and energy into the book.  ... 
doi:10.1515/9783110628616-202 fatcat:gvomr3gvtzaepf6cgbrsvfyyvi

Soft Computational Approaches for Prediction and Estimation of Software Development

Xiao-Zhi Gao, Arun Kumar Sangaiah, Muthu Ramachandran
2016 The Scientific World Journal  
In the paper entitled "Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks," the authors (N. Manikandan and S.  ...  This paper focuses on the architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and artificial neural networks  ...  would like to express our sincere gratitude to all the contributors who have submitted their high-quality manuscripts and to the experts for their support in providing review comments and suggestions on time  ... 
doi:10.1155/2016/3905931 pmid:26989763 pmcid:PMC4775804 fatcat:574zhazhf5f27euaiqrcak3674

Evolving Deep Recurrent Neural Networks Using Ant Colony Optimization [chapter]

Travis Desell, Sophine Clachar, James Higgins, Brandon Wild
2015 Lecture Notes in Computer Science  
Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. International Journal of Forecasting, 27(3):635-660, 2011. 7. G. P. Zhang.  ...  Evolving neural network weights for time-series prediction of general aviation flight data. In T. Bartz-Beielstein, J. Branke, B. Filipi, and J.  ...  Our strategy is easily parallelizable and can use any neural network training algorithm. Evolutionary algorithms can be well applied to training deep recurrent neural networks.  ... 
doi:10.1007/978-3-319-16468-7_8 fatcat:7kg4ppp3g5fdjo5qtnjtfqi36u

Overview of soft intelligent computing technique for supercritical fluid extraction

Sitinoor Adeib Idris, Masturah Markom
2020 International Journal of Advances in Applied Sciences  
Recently there are growing interest in applying smart system or artificial technique to model and simulate a chemical process and also to predict, compute, classify and optimize as well as for process  ...  <span>Optimization of Supercritical Fluid Extraction process with mathematical modeling is essential for industrial applications.  ...  For example, when neural network combines with fuzzy systems, a neuro-fuzzy hybrid will develop and when neural network and evolutionary algorithm combines, a neuro-evolutionary will be developed.  ... 
doi:10.11591/ijaas.v9.i2.pp117-124 fatcat:ha7aav4ykfdznlpu547tmt7neu

Discrete Dynamics in Evolutionary Computation and Its Applications

Yong-Hyuk Kim, Ahmed Kattan, Michael Kampouridis, Yourim Yoon
2016 Discrete Dynamics in Nature and Society  
Evolutionary computation (EC) is considered to be a natural and artificial system with discrete dynamics.  ...  Since various factors affect the fluctuation of network traffic, accurate prediction of network traffic is considered as a challenging task of the time series prediction process.  ... 
doi:10.1155/2016/6043597 fatcat:bgd4kn35qrg25dxp3uwdllbrom

A Comprehensive Survey of Data Mining Techniques on Time Series Data for Rainfall Prediction

Neelam Mishra, Hemant Kumar Soni, Sanjiv Sharma, A.K. Upadhyay
2017 Journal of ICT Research and Applications  
In the present study, a comprehensive survey of data mining approaches and statistical techniques for rainfall prediction on time series data was conducted.  ...  Various techniques are available for prediction and forecasting on the basis of time series data. Presently, the use of data mining techniques for this purpose is increasing day by day.  ...  It was found that statistical techniques and neural network based techniques can be used in parallel in time series data analysis.  ... 
doi:10.5614/itbj.ict.res.appl.2017.11.2.4 fatcat:nmqzd54wtfdihpwsgsgtz7oub4
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