Abstract
This paper proposes a new method for crude oil price forecasting based on support vector machine (SVM). The procedure of developing a support vector machine model for time series forecasting involves data sampling, sample preprocessing, training & learning and out-of-sample forecasting. To evaluate the forecasting ability of SVM, we compare its performance with those of ARIMA and BPNN. The experiment results show that SVM outperforms the other two methods and is a fairly good candidate for the crude oil price prediction.
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Keywords
- Support Vector Machine
- Root Mean Square Error
- Support Vector Regression
- Support Vector Machine Model
- ARIMA Model
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Xie, W., Yu, L., Xu, S., Wang, S. (2006). A New Method for Crude Oil Price Forecasting Based on Support Vector Machines. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758549_63
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DOI: https://doi.org/10.1007/11758549_63
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