Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach

Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach

The paper presents a low complexity recurrent Functional Link Artificial Neural Network for predicting the financial time series data like the stock market indices over a time frame varying from 1 day ahead to 1 month ahead. Although different types of basis functions have been used for low complexi...

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Bibliographic Details
Main Authors: Ajit Kumar Rout, P.K. Dash, Rajashree Dash, Ranjeeta Bisoi
Format: Article
Language:English
Published: Springer 2017-10-01
Series:Journal of King Saud University: Computer and Information Sciences
Subjects:
Low complexity FLANN models
Recurrent computationally efficient FLANN
Differential Evolution
Hybrid Moderate Random Search PSO
Online Access:http://www.sciencedirect.com/science/article/pii/S1319157815000944
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