detecting chaos from agricultural product price time series

Clicks: 244
ID: 185086
2014
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Abstract
Analysis 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.
Reference Key
su2014entropydetecting Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Xin Su;Yi Wang;Shengsen Duan;Junhai Ma
Journal European journal of medicinal chemistry
Year 2014
DOI
10.3390/e16126415
URL
http://www.mdpi.com/1099-4300/16/12/6415
Keywords
chaos time series

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