Detecting Chaos from Agricultural Product Price Time Series
AbstractAnalysis of the characteristics of agricultural product price volatility and trend forecasting are necessary to formulate and implement agricultural price control policies. Taking wholesale cabbage prices as an example, a multiple test methodology has been adopted to identify the nonlinearity, fractality, and chaos of the data. The approaches used include the R/S analysis, the BDS test, the power spectra, the recurrence plot, the largest Lyapunov exponent, the Kolmogorov entropy, and the correlation dimension. The results show that there is chaos in agricultural wholesale price data, which provides a good theoretical basis for selecting reasonable forecasting models as prediction techniques based on chaos theory can be applied to forecasting agricultural prices. View Full-Text
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Su, X.; Wang, Y.; Duan, S.; Ma, J. Detecting Chaos from Agricultural Product Price Time Series. Entropy 2014, 16, 6415-6433.
Su X, Wang Y, Duan S, Ma J. Detecting Chaos from Agricultural Product Price Time Series. Entropy. 2014; 16(12):6415-6433.Chicago/Turabian Style
Su, Xin; Wang, Yi; Duan, Shengsen; Ma, Junhai. 2014. "Detecting Chaos from Agricultural Product Price Time Series." Entropy 16, no. 12: 6415-6433.