Ensemble Decomposition Method for Predicting the Price of Rice in Jakarta

Fransiska, Herlin; Wijayanto, Hari; Sartono, Bagus

doi:10.9790/5728-10419297

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Then, Wijayanto et al (2015) take modification to the approach taken by Kuo to obtain components that reflect the actual pattern and independent of each other. Fransiska et al (2014) had made a good prediction of data decomposition with the EEMD method using ARIMA. In this study, we want to compare the results of predictions against the decomposition data using EEMD and modified EEMD methods.…”

Section: Introductionmentioning

confidence: 99%

Study on Emd Method for Predicting the Price of Curly Red Chili in Indonesia

Zilrahmi

Wijayanto

Afendi

et al. 2020

IJSA

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The fluctuations of curly red chili price affect the inflation rate in Indonesia. So that, the basic characteristics of price movement and correctly prediction for curly red chili price become concern in various studies. Empirical Mode Decomposition (EMD) method helps to examine behavioral characteristics of curly red chili prices in Indonesia easily. Ensemble EMD (EEMD) and modified EEMD are the decomposition method of time series which is development of EMD method. The decomposed data with EMD methods can also used for price forecast. The forecasting with ARIMA and trend polynomial performed to assess the effect of decomposition with EMD methods for forecast stability of curly red chili price in Indonesia under various conditions. The results show the most influence factor for price fluctuation of curly red chili in Indonesia is season and growing season. In this case, the ability of a decomposition method to produce the actual components that describe the pattern of data signals affect the accuracy of the predicted value obtained using the model. The predicted value using the decomposed data by modified EEMD always better than EEMD on the overall condition.

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Section: Introductionmentioning

confidence: 99%