Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting
Financial data is characterized as non-linear, chaotic in nature and volatile thus making the process of forecasting cumbersome. Therefore, a successful forecasting model must be able to capture longterm dependencies from the past chaotic data. In this study, a novel hybrid model, called UKF-NAR...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Book Section |
Published: |
Springer
2014
|
Online Access: | http://eprints.utp.edu.my/11587/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utp.eprints.11587 |
---|---|
record_format |
eprints |
spelling |
my.utp.eprints.115872015-04-28T02:54:12Z Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting Lai, Fong Woon Financial data is characterized as non-linear, chaotic in nature and volatile thus making the process of forecasting cumbersome. Therefore, a successful forecasting model must be able to capture longterm dependencies from the past chaotic data. In this study, a novel hybrid model, called UKF-NARX, consists of unscented kalman filter and non-linear auto-regressive network with exogenous input trained with bayesian regulation algorithm is modelled for chaotic financial forecasting. The proposed hybrid model is compared with commonly used Elman-NARX and static forecasting model employed by financial analysts. Experimental results on Bursa Malaysia KLCI data show that the proposed hybrid model outperforms the other two commonly used models. Springer Rajendra , Prasath Philip , O’Reilly Kathirvalavakumar, T. 2014 Book Section PeerReviewed Lai, Fong Woon (2014) Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting. In: Mining Intelligence and Knowledge Exploration. Springer, London, pp. 72-81. ISBN 978-3-319-13816-9 http://eprints.utp.edu.my/11587/ |
institution |
Universiti Teknologi Petronas |
building |
UTP Resource Centre |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Petronas |
content_source |
UTP Institutional Repository |
url_provider |
http://eprints.utp.edu.my/ |
description |
Financial data is characterized as non-linear, chaotic in nature
and volatile thus making the process of forecasting cumbersome.
Therefore, a successful forecasting model must be able to capture longterm
dependencies from the past chaotic data. In this study, a novel
hybrid model, called UKF-NARX, consists of unscented kalman filter
and non-linear auto-regressive network with exogenous input trained
with bayesian regulation algorithm is modelled for chaotic financial forecasting.
The proposed hybrid model is compared with commonly used
Elman-NARX and static forecasting model employed by financial analysts.
Experimental results on Bursa Malaysia KLCI data show that
the proposed hybrid model outperforms the other two commonly used
models. |
author2 |
Rajendra , Prasath |
author_facet |
Rajendra , Prasath Lai, Fong Woon |
format |
Book Section |
author |
Lai, Fong Woon |
spellingShingle |
Lai, Fong Woon Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting |
author_sort |
Lai, Fong Woon |
title |
Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting |
title_short |
Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting |
title_full |
Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting |
title_fullStr |
Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting |
title_full_unstemmed |
Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting |
title_sort |
hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting |
publisher |
Springer |
publishDate |
2014 |
url |
http://eprints.utp.edu.my/11587/ |
_version_ |
1738655965932158976 |
score |
13.18916 |