Financial time series prediction using polynomial pipelined neural networks

By Abir Jaafar Hussain, Adam Knowles, Paulo J. G. Lisboa, Wael El-Deredy. 2008

57 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Michal Tkác
2. Erdal Kayacan
3. Okyay Kaynak
4. Baris Ulutas
5. Robert Verner

In Computer Vision and Pattern Recognition

1. Okyay Kaynak

In Management Science and Operations Research

1. Michal Tkác
2. Erdal Kayacan
3. Okyay Kaynak
4. Baris Ulutas
5. Robert Verner

Citations in the corpus

• Forecasting S&P 500 index using artificial neural networks and design of experiments (2013, unknown): “The following points justify the use of ANNs to forecast financial time series (Eakins and Stansell 2003; Hornik et al. 1989; Hussain et al. 2007; Lam 2004):”
• Artificial neural networks in business: Two decades of research (2016, background): “But because of its computational demand, Quasi-Newton methods and conjugate gradient were better suited in cases of larger networks [56, 163].”
• Grey system theory-based models in time series prediction (2010, background): “Time series prediction in financial area is generally very difficult because of the factors listed below (Hussain et al., 2008): 1.”