eprintid: 4199
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/41/99
datestamp: 2023-11-09 16:15:52
lastmod: 2023-11-09 16:15:52
status_changed: 2023-11-09 15:57:53
type: conference_item
metadata_visibility: show
creators_name: Abdulkadir, S.J.
creators_name: Yong, S.-P.
title: Empirical analysis of parallel-NARX recurrent network for long-term chaotic financial forecasting
ispublished: pub
keywords: Backpropagation; Electronic trading; Finance; Forecasting, Bayesian regulation; Chaotic time series; Financial forecasting; Long-term forecasting; Mean absolute percentage error; NARX network; Resilient backpropagation; Training algorithms, Recurrent neural networks
note: cited By 19; Conference of 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:112912
abstract: Financial data are characterized by non-linearity, noise, volatility and are chaotic in nature thus making the process of forecasting cumbersome. The main aim of forecasters is to develop an approach that focuses on increasing profit by being able to forecast future stock prices based on current stock data. This paper presents an empirical long term chaotic financial forecasting approach using Parallel non-linear auto-regressive with exogenous input (P-NARX) network trained with Bayesian regulation algorithm. The experimental results based on mean absolute percentage error (MAPE) and other forecasting error metrics shows that P-NARX network trained with Bayesian regulation slightly outperforms Levenberg-marquardt, Resilient back-propagation and one-step-secant training algorithm in forecasting daily Kuala Lumpur Composite Indices. © 2014 IEEE.
date: 2014
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938814444&doi=10.1109%2fICCOINS.2014.6868354&partnerID=40&md5=47595fea8820044f94fd0003f2fb25c7
id_number: 10.1109/ICCOINS.2014.6868354
full_text_status: none
publication: 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2014 - Proceedings
refereed: TRUE
isbn: 9781479943913
citation:   Abdulkadir, S.J. and Yong, S.-P.  (2014) Empirical analysis of parallel-NARX recurrent network for long-term chaotic financial forecasting.  In: UNSPECIFIED.