eprintid: 6278
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/62/78
datestamp: 2023-11-09 16:18:02
lastmod: 2023-11-09 16:18:02
status_changed: 2023-11-09 16:05:31
type: article
metadata_visibility: show
creators_name: Zahari, A.
creators_name: Jaafar, J.
title: Combining Hidden Markov Model and case based reasoning for time series forecasting
ispublished: pub
keywords: Artificial intelligence; Case based reasoning; Economics; Finance; Financial markets; Forecasting; Markov processes; Mean square error; Numerical methods; Soft computing; Time series, Analytical instrument; Computational intelligent techniques; Foreign exchange rates; Multi layer perceptron; Numerical experiments; Root mean squared errors; Softcomputing techniques; Time series forecasting, Hidden Markov models
note: cited By 2; 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:142539
abstract: Hidden Markov Model is one of the most popular and broadly used for representation vastly structured series of data. This paper presents the application of the new approach of Hidden Markov Model and three ensemble nonlinear models to forecasting the foreign exchange rates. The proposed approach and other combination of computational intelligent techniques such as multi layer perceptron, support vector machine are compared with root mean squared error (RMSE) and Mean Absolute Error (MAE) as the performance measures. The results indicate that the new approach of Hidden Markov Model yield the best results consistently over all the currencies. and Case Based Reasoning based ensembles Based on the numerical experiments conducted, it is inferred that using the correct sophisticated ensemble methods in the computational intelligence paradigm can enhance the results obtained by the extent techniques to forecast foreign exchange rates. This suggests that the new approach of HMM is a powerful analytical instrument that is satisfactorily compared to using only the single model and other soft computing techniques for exchange rate predictions. ©Springer-Verlag Berlin Heidelberg 2015.
date: 2015
publisher: Springer Verlag
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84942693786&doi=10.1007%2f978-3-319-17530-0_17&partnerID=40&md5=6313dc4ad4262d5099c958a88e6231b7
id_number: 10.1007/978-3-319-17530-0₁₇
full_text_status: none
publication: Communications in Computer and Information Science
volume: 513
pagerange: 237-247
refereed: TRUE
isbn: 9783319175294
issn: 18650929
citation:   Zahari, A. and Jaafar, J.  (2015) Combining Hidden Markov Model and case based reasoning for time series forecasting.  Communications in Computer and Information Science, 513.  pp. 237-247.  ISSN 18650929