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Coal Price Forecast Based on ARIMA Model

Xiaofan Zhang, Chao Liu, Yuhang Qian

Abstract


This paper analyzes and determines the decision variables and constraints, establishes the EECM-ARAMA model to analyze and research coal price forecasts. Firstly, we first confirm the influencing factors. Then, we conduct correlation coefficient tests on price and various factors, and get the strength of the correlation between each factor and price. The second is to establish a coal price prediction model. Firstly, we use the EEMD method to transform the original price series into a stable time series, and then formulate three ARIMA models by comparing the size of the influencing factors and the parameter estimation results. After testing, we finally choose the ARIMA 03 model to predict the next 31 days, 35 Weekly and 36-month coal prices. Finally, we combine the models and ideas of the above issues to obtain factors that affect coal price changes and related price prediction models, and combine experience to give some feasible policy recommendations.

Keywords


Coal Price Forecast; ARAMA Model; EECM Algorithm

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References


Sun J, Peng J. Application of temporal sequence analysis technique in coal price prediction. Geology, Technology and Economic Management 2000; (3): 33–40.

Ning Y, Zhang D, Li X. Research and application of compound wavelet neural network model for price prediction in international coal market (in Chinese). Coal Economic Research 2001; 6(7): 16–19.

Zhang H, Jiang Z. The establishment and forecasting analysis of the ARIMA model of coal prices in China (in Chinese). Industrial Technology Economy 2007; 26(7): 102–105.

Ding Z. Research on the influence effect of coal price fluctuation on China entity economy [PhD thesis]. Beijing: China University of Mining and Technology; 2011.




DOI: http://dx.doi.org/10.18282/ff.v9i4.1530

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