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Information Journal Paper

Title

COMPARISON OF NEURAL NETWORKS AND ARIMA IN MODELING AND FORECASTING OF SHORT RUN PRICING OF THE OPEC CRUDE OIL BASKET (WITH FOCUS ON COMPARATIVE EXPECTATIONS)

Pages

  25-47

Abstract

 Oil price has a determining role in the World Economy due to its strategic role in international production systems. This is why there has been a lot of interest in understanding its price dynamics and modeling its behavior and factors that lead to its volatility. In this context, Artificial NEURAL NETWORKs have a high potential in modeling of random and complex processes and FORECASTING of dynamic nonlinear trends. In this paper we model and forecast the daily price of the OPEC oil basket through use of Artificial NEURAL NETWORKs based on daily price expectations and compare its results with those of the ARIMA model based on prediction accuracy measure criteria. The results demonstrate that NEURAL NETWORKs have better FORECASTING power than the ARIMA model, with the daily price of oil being a function of the price in the preceding five days.

Cites

References

Cite

APA: Copy

SADEGHI, HOSIN, ZOLFAGHARI, MEHDI, & ELHAMINEZHD, MOJTABA. (2011). COMPARISON OF NEURAL NETWORKS AND ARIMA IN MODELING AND FORECASTING OF SHORT RUN PRICING OF THE OPEC CRUDE OIL BASKET (WITH FOCUS ON COMPARATIVE EXPECTATIONS). ENERGY ECONOMICS REVIEW, 8(28), 25-47. SID. https://sid.ir/paper/99461/en

Vancouver: Copy

SADEGHI HOSIN, ZOLFAGHARI MEHDI, ELHAMINEZHD MOJTABA. COMPARISON OF NEURAL NETWORKS AND ARIMA IN MODELING AND FORECASTING OF SHORT RUN PRICING OF THE OPEC CRUDE OIL BASKET (WITH FOCUS ON COMPARATIVE EXPECTATIONS). ENERGY ECONOMICS REVIEW[Internet]. 2011;8(28):25-47. Available from: https://sid.ir/paper/99461/en

IEEE: Copy

HOSIN SADEGHI, MEHDI ZOLFAGHARI, and MOJTABA ELHAMINEZHD, “COMPARISON OF NEURAL NETWORKS AND ARIMA IN MODELING AND FORECASTING OF SHORT RUN PRICING OF THE OPEC CRUDE OIL BASKET (WITH FOCUS ON COMPARATIVE EXPECTATIONS),” ENERGY ECONOMICS REVIEW, vol. 8, no. 28, pp. 25–47, 2011, [Online]. Available: https://sid.ir/paper/99461/en

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