Forecasting of oil price is an important area of energy market research. Based on the idea of decomposition-reconstruction-integration, this paper built a new multiscale combined forecasting model with the methods of empirical mode decomposition (EMD), artificial neural network (ANN), support vector machine (SVM), and time series methods. While building the model, we proposed a new idea to use run length judgment method to reconstruct the component sequences. Then this model was applied to analyze the fluctuation and trend of international oil price. Oil price series was decomposed and reconstructed into high frequency, medium frequency, low frequency, and trend sequences. Different features of fluctuation can be explained by irregular factors, season factors, major events, and long-term trend. Empirical analysis showed that the multiscale combined model obtained the best forecasting result compared with single models including ARIMA, Elman, SVM, and GARCH and combined models including ARIMA-SVM model and EMD-SVM-SVM method.
Abstract. The dependence on foreign trade of China and European Union are both at a relatively high level, and the space for Sino-EU trade is still enormous. So, this paper adopts cointegration analysis and error correction model to study the relationship between Sino-EU trade and economic growth from a long-term and short-term perspective respectively. Cointegration analysis shows that: whether it is Sino-EU trade and China's economic growth or Sino-EU trade and EU's economic growth, there exists long-term cointegration relationship between them. Sino-EU trade plays a more important role in economic growth of China than its role in stimulating economic growth of European Union. Error correction model demonstrates that Sino-EU trade may deviate from its long-run equilibrium with economic growth in the short run.
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