Universiti Teknologi Malaysia Institutional Repository

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

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.

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Official URL: http://dx.doi.org/10.1063/1.4915840

Abstract

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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:EMD, fishery landing, forecasting
Subjects:Q Science > QA Mathematics
Divisions:Science
ID Code:59271
Deposited By: Haliza Zainal
Deposited On:18 Jan 2017 01:50
Last Modified:05 Aug 2021 02:26

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