Research On Prediction For Uranium Price Seires Based On Intelligent Computaiton And Chaotic Theory

Uranium resource products have been widely used in economics, military, social lives,and so on and have a revolutionary effect on the real world in many areas. Uranium resourceis both the material basis for the development of nuclear energy and a kind of strategicresource.Therefore, more accurate forecasts for international uranium resource prices play anincreasingly important role in the development planning and utilization of nuclear energy.Forecasting the uranium resource prices is one of the most important and challenging tasksdue to its inherent nonlinearity and non-stationary characteristics.Based on chaos theory and empirical mode decomposition technique, several hybridintelligence prediction models for international uranium resource prices forecasting have beenestablished combining with some intelligent computation methods such as Least SquareSupport Vector Machine(LSSVM), Dynamic Fuzzy Neural Network(DFNN), ExtremeLearning Machine(ELM) and Relevance Vector Machine(RVM). The main contents discussedin this thesis are described as follows:1?The influence of phase space parameters on phase space quality and the commonmethods for determining delay time and embedding dimension are discussed based on thereconstruction theory. In view of parameter selection problem of phase space reconstructionfor the actual uranium price time series, a novel method for determining the set of parametersfor a phase space representation of a time series named as the entropy ratio(ER) method isintroduced. Based upon the differential entropy, both the optimal embedding dimension andtime lag are simultaneously determined.2? Nonlinearity and chaotic characteristic for uranium price series have beenidentification. On the one hand, nonlinear nature of the time series is examined by performingdelay vector variance (DVV) analyses on both the original and a number of surrogate timeseries, using the optimal embedding dimension of the original time series. On the other hand,quantitative calculation about saturation correlation dimension and the largest Lyapunovexponent of uranium price series is used to identity their chaotic characteristics.3?Based on the phase reconstruction, a prediction model for international uraniumresource prices based on DFNN combined with Quantum-behaved Particle SwarmOptimization(QPSO) were proposed. The salient characteristics of the method are:1)hierarchical on-line self-organizing learning is used;2)neurons can be recruited or deleteddynamically according to their significance to the system's performance; and3)fast learningspeed can be achieved.4?A hybrid forecasting approach combining empirical mode decomposition(EMD),phase space reconstruction(PSR), and ELM for international uranium resource prices isproposed. In the first stage, the original uranium resource price series are first decomposedinto a finite number of independent intrinsic mode functions(IMFs), with different frequencies.In the second stage, the IMFs are composed into three subseries based on the fine-to-coarsereconstruction rule. In the third stage, based on phase space reconstruction, different ELMmodels are used to model and forecast the three subseries, respectively, according to the intrinsic characteristic time scales. Finally, in the fourth stage, these forecasting results arecombined to output the ultimate forecasting result. Experimental results from real uraniumresource price data demonstrate that the proposed hybrid forecasting method outperformsRBF neuralnetwork(RBFNN) and single ELM in terms of RMSE, MAE, and DS.5?A novel hybrid ensemble modeling approach integrating ensemble empirical modedecomposition(EEMD) and multiple intelligent computation methods such as RVM, LSSVMand ELM is proposed for international uranium resource prices forecasting, based on thephase reconstruction. This hybrid approach is formulated specifcally to address difficulties inmodeling uranium resource prices forecasting, which has inherently complexity andirregularity. In the proposed hybrid ensemble learning paradigm, EEMD, as a competitivedecomposition method in stead of EMD, is first applied to decompose original data ofinternational uranium resource prices into a number of independent intrinsic modefunctions(IMFs) of original data. Then the IMFs are composed into three subseries based onthe fine-to-coarse reconstruction rule. Based on phase space reconstruction, different modelsincluding RVM, LSSVM, ELM are used to model and forecast the three subseries,respectively, according to the intrinsic characteristic time scales. Finally, these predictedsubseries are aggregated into an ensemble result as final prediction. Empirical resultsdemonstrate that the hybrid modeling approach can outperform some other popularforecasting models in both level prediction and directional forecasting.6?Based on the phase reconstruction, the time series of uranium resource price isexpanded to multivariate time series, which includes ergodic information, and so that moreabundant information can be found in favor of model training. A hybrid model combiningMLR and LSSVM is established. The proposed hybrid model exploits the unique strength ofMLR and LSSVM models in forecasting uranium resource prices. In addition, particle swarmoptimization (PSO) is used to find the optimal parameters of LSSVM in order to improve theprediction accuracy. Experimental results indicate that the proposed model outperforms sometraditional models, including MLR, RBFNN, LSSVM, the linear combination model, and themodel incorporating MLR and RBFNN.