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Entropy 2016, 18(12), 439; doi:10.3390/e18120439

The Optimal Confidence Intervals for Agricultural Products’ Price Forecasts Based on Hierarchical Historical Errors

1
,
2,* and 3,*
1
School of Mathematics and Quantitative Economics, Shandong University of Finance and Economics, Jinan 250014, China
2
School of MBA, Shandong University of Finance and Economics, Jinan 250014, China
3
College of Management and Economics, Tianjin University, Tianjin 300072, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 14 October 2016 / Revised: 28 November 2016 / Accepted: 29 November 2016 / Published: 8 December 2016
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Abstract

With the levels of confidence and system complexity, interval forecasts and entropy analysis can deliver more information than point forecasts. In this paper, we take receivers’ demands as our starting point, use the trade-off model between accuracy and informativeness as the criterion to construct the optimal confidence interval, derive the theoretical formula of the optimal confidence interval and propose a practical and efficient algorithm based on entropy theory and complexity theory. In order to improve the estimation precision of the error distribution, the point prediction errors are STRATIFIED according to prices and the complexity of the system; the corresponding prediction error samples are obtained by the prices stratification; and the error distributions are estimated by the kernel function method and the stability of the system. In a stable and orderly environment for price forecasting, we obtain point prediction error samples by the weighted local region and RBF (Radial basis function) neural network methods, forecast the intervals of the soybean meal and non-GMO (Genetically Modified Organism) soybean continuous futures closing prices and implement unconditional coverage, independence and conditional coverage tests for the simulation results. The empirical results are compared from various interval evaluation indicators, different levels of noise, several target confidence levels and different point prediction methods. The analysis shows that the optimal interval construction method is better than the equal probability method and the shortest interval method and has good anti-noise ability with the reduction of system entropy; the hierarchical estimation error method can obtain higher accuracy and better interval estimation than the non-hierarchical method in a stable system. View Full-Text
Keywords: optimal confidence interval; entropy; algorithm; error distribution; hierarchical by price; agricultural products’ price; anti-noise ability optimal confidence interval; entropy; algorithm; error distribution; hierarchical by price; agricultural products’ price; anti-noise ability
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, Y.; Su, X.; Guo, S. The Optimal Confidence Intervals for Agricultural Products’ Price Forecasts Based on Hierarchical Historical Errors. Entropy 2016, 18, 439.

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