2016
DOI: 10.12988/ams.2016.6389
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A modified EMD-ARIMA based on clustering analysis for fishery landing forecasting

Abstract: This paper investigates the ability of a new hybrid forecasting model based on empirical mode decomposition (EMD), cluster analysis and Autoregressive Integrated Moving Average (ARIMA) model to improve the accuracy of fishery landing forecasting. In the first step, the original fishery landing was decomposed into a finite number of Intrinsic Mode Functions (IMFs) and a residual by EMD. The second stage, the cluster analysis was used to reconstruct the IMFs and residual into high frequency, medium frequency and… Show more

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Cited by 3 publications
(4 citation statements)
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“…Comparing ARIMA, EWT-ARIMA and modified EWT-ARIMA, it is generally safe to assume the proposed modified EWT-ARIMA performs the best, since it preformed better on 3 out of the 5 SPI tested. The result agrees with previous reseach that has proven that clustering on IMFs from EMD improves the forecast accuracy [18]- [20]. Implementation of fuzzy c-means clustering improves the forecast accuracy by grouping the IMFs according to its structure based on the intrinsic frequency of the IMFs.…”
Section: Performance Evaluationsupporting
confidence: 91%
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“…Comparing ARIMA, EWT-ARIMA and modified EWT-ARIMA, it is generally safe to assume the proposed modified EWT-ARIMA performs the best, since it preformed better on 3 out of the 5 SPI tested. The result agrees with previous reseach that has proven that clustering on IMFs from EMD improves the forecast accuracy [18]- [20]. Implementation of fuzzy c-means clustering improves the forecast accuracy by grouping the IMFs according to its structure based on the intrinsic frequency of the IMFs.…”
Section: Performance Evaluationsupporting
confidence: 91%
“…The number of cluseter chosen is 3. Several studies have used 3 cluster [17], [18]. Figure 7 shows the combined into 3 wavelets based on the result of FCM clustering.…”
Section: Ewt-arima Clusteringmentioning
confidence: 99%
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“…Bouras (2015) used three individual forecasting models which are the integrated autoregressive moving average (ARIMA), generalize autoregressive conditional heteroscedasticity (GARCH) and Census X-II models to predict the Moroccan coastal fish production. Mini, Kuriakose, & Sathianandan (2015) studied the quarterly fish landing along Northeast coast of India by comparing the following univariate models which are the Holt-Winter's, ARIMA and neural network autoregression (NNAR) models while in Malaysia, Shabri (2016) proposed a new model, MEMD-ARIMA model in his study by considering the monthly fish landing for East Johor. In a previous study, when the ARIMA and fractionally integrated autoregressive moving average (ARFIMA) models were used in forecasting demersal and pelagic marine fish production in Malaysia, the ARFIMA model was found to be the better model (Shitan, Wee, Chin, & Siew, 2008).…”
Section: Introductionmentioning
confidence: 99%