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Time series forecasting using least square support vector machine for Canadian Lynx data

Time series analysis and forecasting is an active research area over the last few decades. There are various kinds of forecasting models have been developed and researchers have relied on statistical techniques to predict the future. This paper discusses the application of Least Square Support Vecto...

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Main Authors: Shabri, Ani, Ismail, Shuhaida
Format: Article
Published: Penerbit UTM Press 2014
Subjects:
Q Science
Online Access:http://eprints.utm.my/id/eprint/63088/
https://dx.doi.org/10.11113/jt.v70.3510
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spelling my.utm.630882017-06-14T03:12:24Z http://eprints.utm.my/id/eprint/63088/ Time series forecasting using least square support vector machine for Canadian Lynx data Shabri, Ani Ismail, Shuhaida Q Science Time series analysis and forecasting is an active research area over the last few decades. There are various kinds of forecasting models have been developed and researchers have relied on statistical techniques to predict the future. This paper discusses the application of Least Square Support Vector Machine (LSSVM) models for Canadian Lynx forecasting. The objective of this paper is to examine the flexibility of LSSVM in time series forecasting by comparing it with other models in previous research such as Artificial Neural Networks (ANN), Auto-Regressive Integrated Moving Average (ARIMA), Feed-Forward Neural Networks (FNN), Self-Exciting Threshold Auto-Regression (SETAR), Zhang’s model, Aladang’s hybrid model and Support Vector Regression (SVR) model. The experiment results show that the LSSVM model outperforms the other models based on the criteria of Mean Absolute Error (MAE) and Mean Square Error (MSE). It also indicates that LSSVM provides a promising alternative technique in time series forecasting. Penerbit UTM Press 2014 Article PeerReviewed Shabri, Ani and Ismail, Shuhaida (2014) Time series forecasting using least square support vector machine for Canadian Lynx data. Jurnal Teknologi, 70 (5). pp. 11-15. ISSN 0127-9696 https://dx.doi.org/10.11113/jt.v70.3510 DOI:10.11113/jt.v70.3510
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science
spellingShingle Q Science
Shabri, Ani
Ismail, Shuhaida
Time series forecasting using least square support vector machine for Canadian Lynx data
description Time series analysis and forecasting is an active research area over the last few decades. There are various kinds of forecasting models have been developed and researchers have relied on statistical techniques to predict the future. This paper discusses the application of Least Square Support Vector Machine (LSSVM) models for Canadian Lynx forecasting. The objective of this paper is to examine the flexibility of LSSVM in time series forecasting by comparing it with other models in previous research such as Artificial Neural Networks (ANN), Auto-Regressive Integrated Moving Average (ARIMA), Feed-Forward Neural Networks (FNN), Self-Exciting Threshold Auto-Regression (SETAR), Zhang’s model, Aladang’s hybrid model and Support Vector Regression (SVR) model. The experiment results show that the LSSVM model outperforms the other models based on the criteria of Mean Absolute Error (MAE) and Mean Square Error (MSE). It also indicates that LSSVM provides a promising alternative technique in time series forecasting.
format Article
author Shabri, Ani
Ismail, Shuhaida
author_facet Shabri, Ani
Ismail, Shuhaida
author_sort Shabri, Ani
title Time series forecasting using least square support vector machine for Canadian Lynx data
title_short Time series forecasting using least square support vector machine for Canadian Lynx data
title_full Time series forecasting using least square support vector machine for Canadian Lynx data
title_fullStr Time series forecasting using least square support vector machine for Canadian Lynx data
title_full_unstemmed Time series forecasting using least square support vector machine for Canadian Lynx data
title_sort time series forecasting using least square support vector machine for canadian lynx data
publisher Penerbit UTM Press
publishDate 2014
url http://eprints.utm.my/id/eprint/63088/
https://dx.doi.org/10.11113/jt.v70.3510
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score 13.214268

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