eprintid: 4524
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/45/24
datestamp: 2023-11-09 16:16:13
lastmod: 2023-11-09 16:16:13
status_changed: 2023-11-09 15:58:37
type: article
metadata_visibility: show
creators_name: Zahari, A.A.
creators_name: Jaafar, J.
title: Hybridization of hidden markov model and case based reasoning for time series forecasting
ispublished: pub
keywords: Case based reasoning; Finance; Forecasting; Markov processes; Mean square error; Time series, Baum-Welch algorithms; Convergence/divergence; Forecasting accuracy; Root mean square errors; Short-term trading; Statistical performance; Technical models; Time series forecasting, Hidden Markov models
note: cited By 0; Conference of 13th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2014 ; Conference Date: 22 September 2014 Through 24 September 2014; Conference Code:116901
abstract: 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.
date: 2014
publisher: IOS Press
official_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
id_number: 10.3233/978-1-61499-434-3-63
full_text_status: none
publication: Frontiers in Artificial Intelligence and Applications
volume: 265
pagerange: 63-74
refereed: TRUE
isbn: 9781614994336
issn: 09226389
citation:   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.  ISSN 09226389