Tin ore is one of the dominant minerals in China. It was listed in the strategic mineral catalogue in National Mineral Resources Planning (2016Planning ( -2020. Based on the perspective of complex network, this paper selects international tin ore trade data from 2007 to 2018 of the United Nations commodity trade database as sample, constructs a directional weighted international tin ore trade complex network, and quantitatively analyzes the characteristics and time evolution of international tin ore trade from aspects of overall structural characteristics of trade network, power-law distribution characteristics, core country identification and propagation path of supply risk, etc.. The results show that: (1) From 2007 to 2018, the global tin ore trade activity has been greatly affected twice, which are the shrinkage of tin ore trade caused by the global financial crisis in 2008 and the impact of Indonesia's tin export tightening policy between 2012 and 2016; (2) As a transit country, Singapore's import concentration rate is very high while its export shows diversified characteristics; (3) By constructing a spanning tree, the supply risk propagation and path diffusion as well as characteristics of Indonesia which is the largest tin ore export country are captured under current trade pattern; (4) By analyzing intermediary nature, developed countries in Europe and the United States play crucial roles in controlling the stability of global tin ore trade.
Time series forecasting for financial market has increasingly attracted the interests of investors and academic researchers. In recent years, some hybrid models have been constructed to improve the predictions since some methods cannot extract useful information from stock time series with noise to conduct prediction. In this research, a prediction framework is proposed to forecast the stock market behavior using the methods of wavelet coherence, multiscale decomposition and support vector regression (SVR). First, a combined method is applied to the raw data and remove noise to get useful information. Then, a SVR model is applied to improve the prediction performance of multidimensional nonlinear data. Furthermore, the comparison experiments were performed with both Shanghai Composite Index and Dow Jones Index to examine the effectiveness of the framework. The results indicate that the proposed framework performs better than other advanced models.
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