eprintid: 6357
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/63/57
datestamp: 2023-11-09 16:18:08
lastmod: 2023-11-09 16:18:08
status_changed: 2023-11-09 16:05:48
type: conference_item
metadata_visibility: show
creators_name: Zahari, A.
creators_name: Jaafar, J.
title: A novel approach of hidden markov model for time series forecasting
ispublished: pub
keywords: Finance; Forecasting; Hidden Markov models; Information management; Markov processes; Mean square error; Time series, Baum-Welch algorithms; Benchmark models; Decision models; Forecasting accuracy; Foreign exchange; Root mean square errors; Statistical performance; Time series forecasting, Case based reasoning
note: cited By 4; Conference of 9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015 ; Conference Date: 8 January 2015 Through 10 January 2015; Conference Code:111491
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 single HMM and HMM ensemble with neural network. HMM is trained by using forward-backward 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 March 2014. The preliminary results indicate that the new approach of HMM produce the lowest RMSE compared to the benchmark models. Further study is to adopt HMM-CBR in testing of GBP/USD, GBP/JPY, USD/JPY, and EUR/JPY exchange rate.
date: 2015
publisher: Association for Computing Machinery, Inc
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926140234&doi=10.1145%2f2701126.2701179&partnerID=40&md5=1c77c6614801c6dc47bb6b0b81384033
id_number: 10.1145/2701126.2701179
full_text_status: none
publication: ACM IMCOM 2015 - Proceedings
refereed: TRUE
isbn: 9781450333771
citation:   Zahari, A. and Jaafar, J.  (2015) A novel approach of hidden markov model for time series forecasting.  In: UNSPECIFIED.