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Improving Crude Oil Price Forecasting Accuracy via Decomposition and Ensemble Model by Reconstructing the Stochastic and Deterministic Influences

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This paper focuses on designing the model which enhanced the forecasting accuracy of the time series by determining the deterministic and stochastic influences of the observations. In first step, Ensemble Empirical Mode Decomposition (EEMD) is used to split the time series into several modes. In the second step the decompose modes be reconstructed into two components i.e., stochastic and deterministic. The stochastic component modes are modeled individually because of the high variability among the observations of each mode while the deterministic component is treated as a single component. In the last step, the best Auto-regressive Integrated Moving Average (ARIMA) model is selected for reconstructed modes and combine the results for the last output. The empirical study of two daily crude oil prices confirmed the effectiveness of the proposed approach.
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Keywords: ARIMA; Autocorrelation; Crude Oil Prices; EEMD; Forecasting

Document Type: Research Article

Affiliations: Mathematical Sciences Department, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia

Publication date: June 1, 2018

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