Reference Hub

This research has been cited in:

Article
Designing an efficient predictor model using PSNN and crow search based optimization technique for gold price predictionIntelligent Decision Technologies10.3233/IDT-200093
Conference
Enhancing performance of a LENN based CCFD model with a hybrid TOPSIS-ReliefF based feature selection technique2022 2nd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON)10.1109/ODICON54453.2022.10009935
Article
Evaluating chaotic functions with flower pollination algorithm for modelling an optimized low complexity neural network based NAV predictor modelSoft Computing10.1007/s00500-022-07257-8
Article
DECPNN: A hybrid stock predictor model using Differential Evolution and Chebyshev Polynomial neural networkIntelligent Decision Technologies10.3233/IDT-170313
Chapter
Data-Driven Trading Strategies and ApplicationsProceedings of International Conference on Communication and Computational Technologies10.1007/978-981-99-3485-0_56
Article
Short term wind speed prediction using multiple kernel pseudo inverse neural networkInternational Journal of Automation and Computing10.1007/s11633-017-1086-7
Article
PSO with Dynamic Adaptation of Parameters for Optimization in Neural Networks with Interval Type-2 Fuzzy Numbers WeightsAxioms10.3390/axioms8010014
Chapter
A Legendre Neural Network for Credit Card Fraud DetectionIntelligent and Cloud Computing10.1007/978-981-15-6202-0_42
Article
Performance analysis of an evolutionary recurrent Legendre Polynomial Neural Network in application to FOREX predictionJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2017.12.010
Article
A Comprehensive Survey on Higher Order Neural Networks and Evolutionary Optimization Learning Algorithms in Financial Time Series ForecastingArchives of Computational Methods in Engineering10.1007/s11831-023-09942-9
Article
Performance analysis of a higher order neural network with an improved shuffled frog leaping algorithm for currency exchange rate predictionApplied Soft Computing10.1016/j.asoc.2018.02.043
Chapter
Predicting the Price of Gold: A CSPNN-DE ModelIntelligent and Cloud Computing10.1007/978-981-15-6202-0_29
Article
Utility of a Shuffled Differential Evolution algorithm in designing of a Pi-Sigma Neural Network based predictor modelApplied Computing and Informatics10.1016/j.aci.2019.04.001
Conference
A novel COVID-19 predictor model inspired by Flower Pollination Algorithm2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS)10.1109/MLCSS57186.2022.00045
Article
An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate predictionPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2017.05.044
Chapter
Time Series Prediction Using Complex-Valued Legendre Neural Network with Different Activation FunctionsIntelligent Computing Methodologies10.1007/978-3-319-95957-3_22
Article
A novel chaotic flower pollination algorithm for modelling an optimized low-complexity neural network-based NAV predictor modelProgress in Artificial Intelligence10.1007/s13748-022-00289-z
Chapter
MDHS–LPNN: A Hybrid FOREX Predictor Model Using a Legendre Polynomial Neural Network with a Modified Differential Harmony Search TechniqueHandbook of Neural Computation10.1016/B978-0-12-811318-9.00025-9
Chapter
Hybrid Honey Bees Meta-Heuristic for Benchmark Data ClassificationExploring Critical Approaches of Evolutionary Computation10.4018/978-1-5225-5832-3.ch008

Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN

Rajashree Dash, Pradipta Kishore Dash

Source Title: International Journal of Applied Evolutionary Computation (IJAEC)7(1)

ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781466690806|DOI: 10.4018/IJAEC.2016010102

Cite Article Cite Article

MLA

Dash, Rajashree, and Pradipta Kishore Dash. "Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN." IJAEC vol.7, no.1 2016: pp.16-32. http://doi.org/10.4018/IJAEC.2016010102

APA

Dash, R. & Dash, P. K. (2016). Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN. International Journal of Applied Evolutionary Computation (IJAEC), 7(1), 16-32. http://doi.org/10.4018/IJAEC.2016010102

Chicago

Dash, Rajashree, and Pradipta Kishore Dash. "Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN," International Journal of Applied Evolutionary Computation (IJAEC) 7, no.1: 16-32. http://doi.org/10.4018/IJAEC.2016010102

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

In this paper a predictor model using Legendre Neural Network is proposed for one day ahead prediction of financial time series data. The Legendre Neural Network (LENN) is a single layer structure that possess faster convergence rate and reduced computational complexity by increasing the dimensionality of the input pattern with a set of linearly independent nonlinear functions. The parameters of the LENN model are estimated using a Moderate Random Search Particle Swarm Optimization Method (HMRPSO). The HMRPSO is a variant of PSO that uses a moderate random search method to enhance the global search ability of particles and increases their convergence rates by focusing on valuable search space regions. Training LENN using HMRPSO has also been compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of LENN for predicting the Bombay Stock Exchange and S&P 500 data sets.

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN

MLA

APA

Chicago

Export Reference

Abstract

Request Access