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Fishery landing forecasting using EMD-based least square support vector machine models

In this paper, the novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EMD) and least square support machine (LSSVM) is proposed to improve the accuracy of fishery landing forecasting. This hybrid is formulated specifically to address in modeling fishery landin...

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Main Author: Shabri, Ani
Format: Conference or Workshop Item
Published: 2015
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/59271/
http://dx.doi.org/10.1063/1.4915840
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spelling my.utm.592712021-08-05T02:26:34Z http://eprints.utm.my/id/eprint/59271/ Fishery landing forecasting using EMD-based least square support vector machine models Shabri, Ani QA Mathematics In this paper, the novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EMD) and least square support machine (LSSVM) is proposed to improve the accuracy of fishery landing forecasting. This hybrid is formulated specifically to address in modeling fishery landing, which has high nonlinear, non-stationary and seasonality time series which can hardly be properly modelled and accurately forecasted by traditional statistical models. In the hybrid model, EMD is used to decompose original data into a finite and often small number of sub-series. The each sub-series is modeled and forecasted by a LSSVM model. Finally the forecast of fishery landing is obtained by aggregating all forecasting results of sub-series. To assess the effectiveness and predictability of EMD-LSSVM, monthly fishery landing record data from East Johor of Peninsular Malaysia, have been used as a case study. The result shows that proposed model yield better forecasts than Autoregressive Integrated Moving Average (ARIMA), LSSVM and EMD-ARIMA models on several criteria. 2015-05-15 Conference or Workshop Item PeerReviewed Shabri, Ani (2015) Fishery landing forecasting using EMD-based least square support vector machine models. In: International Conference on Mathematics, Engineering and Industrial Applications, ICoMEIA 2014, 28 May 2014 - 30 May 2014, Penang, Malaysia. http://dx.doi.org/10.1063/1.4915840
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 QA Mathematics
spellingShingle QA Mathematics
Shabri, Ani
Fishery landing forecasting using EMD-based least square support vector machine models
description In this paper, the novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EMD) and least square support machine (LSSVM) is proposed to improve the accuracy of fishery landing forecasting. This hybrid is formulated specifically to address in modeling fishery landing, which has high nonlinear, non-stationary and seasonality time series which can hardly be properly modelled and accurately forecasted by traditional statistical models. In the hybrid model, EMD is used to decompose original data into a finite and often small number of sub-series. The each sub-series is modeled and forecasted by a LSSVM model. Finally the forecast of fishery landing is obtained by aggregating all forecasting results of sub-series. To assess the effectiveness and predictability of EMD-LSSVM, monthly fishery landing record data from East Johor of Peninsular Malaysia, have been used as a case study. The result shows that proposed model yield better forecasts than Autoregressive Integrated Moving Average (ARIMA), LSSVM and EMD-ARIMA models on several criteria.
format Conference or Workshop Item
author Shabri, Ani
author_facet Shabri, Ani
author_sort Shabri, Ani
title Fishery landing forecasting using EMD-based least square support vector machine models
title_short Fishery landing forecasting using EMD-based least square support vector machine models
title_full Fishery landing forecasting using EMD-based least square support vector machine models
title_fullStr Fishery landing forecasting using EMD-based least square support vector machine models
title_full_unstemmed Fishery landing forecasting using EMD-based least square support vector machine models
title_sort fishery landing forecasting using emd-based least square support vector machine models
publishDate 2015
url http://eprints.utm.my/id/eprint/59271/
http://dx.doi.org/10.1063/1.4915840
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score 13.211869

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