Hybridization of hidden markov model and case based reasoning for time series forecasting
In the past few years, tremendous studies have been made to examine the accuracy of time series forecasting that provide the foundation for decision models in foreign exchange data. This study proposes a novel approach of Hidden Markov Model and Case Based reasoning for time series forecasting. This...
Saved in:
Main Authors: | , |
---|---|
Format: | Article |
Published: |
IOS Press
2014
|
Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948779989&doi=10.3233%2f978-1-61499-434-3-63&partnerID=40&md5=0bc1aeb3d0d4fcf4ca48cdcbba0444fc http://eprints.utp.edu.my/31729/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Universiti Teknologi Petronas |
id |
my.utp.eprints.31729 |
---|---|
record_format |
eprints |
spelling |
my.utp.eprints.317292022-03-29T03:36:09Z Hybridization of hidden markov model and case based reasoning for time series forecasting Zahari, A.A. Jaafar, J. In the past few years, tremendous studies have been made to examine the accuracy of time series forecasting that provide the foundation for decision models in foreign exchange data. This study proposes a novel approach of Hidden Markov Model and Case Based reasoning for time series forecasting. This paper compares the proposed method with the technical models; moving average convergence/divergence model (MACD), William's percent range, and naïve strategy for short-term trading decision. HMM is trained by using forwardbackward or Baum-Welch algorithm and the likelihood value is used to predict future exchange rate price. The forecasting accuracy has been measured according to Root Mean Square Error (RMSE). The statistical performance of all techniques is investigated in testing of EUR/USD exchange rate time series over the period of October 2010 to November 2013. The preliminary results indicate that the new approach of HMM produce the lowest RMSE compared to the benchmark models. Further study is to adopt Case Based reasoning to further improve the forecasting results. © 2014 The authors and IOS Press. All rights reserved. IOS Press 2014 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948779989&doi=10.3233%2f978-1-61499-434-3-63&partnerID=40&md5=0bc1aeb3d0d4fcf4ca48cdcbba0444fc Zahari, A.A. and Jaafar, J. (2014) Hybridization of hidden markov model and case based reasoning for time series forecasting. Frontiers in Artificial Intelligence and Applications, 265 . pp. 63-74. http://eprints.utp.edu.my/31729/ |
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 |
In the past few years, tremendous studies have been made to examine the accuracy of time series forecasting that provide the foundation for decision models in foreign exchange data. This study proposes a novel approach of Hidden Markov Model and Case Based reasoning for time series forecasting. This paper compares the proposed method with the technical models; moving average convergence/divergence model (MACD), William's percent range, and naïve strategy for short-term trading decision. HMM is trained by using forwardbackward or Baum-Welch algorithm and the likelihood value is used to predict future exchange rate price. The forecasting accuracy has been measured according to Root Mean Square Error (RMSE). The statistical performance of all techniques is investigated in testing of EUR/USD exchange rate time series over the period of October 2010 to November 2013. The preliminary results indicate that the new approach of HMM produce the lowest RMSE compared to the benchmark models. Further study is to adopt Case Based reasoning to further improve the forecasting results. © 2014 The authors and IOS Press. All rights reserved. |
format |
Article |
author |
Zahari, A.A. Jaafar, J. |
spellingShingle |
Zahari, A.A. Jaafar, J. Hybridization of hidden markov model and case based reasoning for time series forecasting |
author_facet |
Zahari, A.A. Jaafar, J. |
author_sort |
Zahari, A.A. |
title |
Hybridization of hidden markov model and case based reasoning for time series forecasting |
title_short |
Hybridization of hidden markov model and case based reasoning for time series forecasting |
title_full |
Hybridization of hidden markov model and case based reasoning for time series forecasting |
title_fullStr |
Hybridization of hidden markov model and case based reasoning for time series forecasting |
title_full_unstemmed |
Hybridization of hidden markov model and case based reasoning for time series forecasting |
title_sort |
hybridization of hidden markov model and case based reasoning for time series forecasting |
publisher |
IOS Press |
publishDate |
2014 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948779989&doi=10.3233%2f978-1-61499-434-3-63&partnerID=40&md5=0bc1aeb3d0d4fcf4ca48cdcbba0444fc http://eprints.utp.edu.my/31729/ |
_version_ |
1738657288340635648 |