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Realization of neural network research and agricultural futures forecasting model - based on principal component

Author: HuangYing
Tutor: BaiMei
School: Capital Normal University
Course: Applied Computer Technology
Keywords: futures forecasting principal component design - neural network BP MATLAB
CLC: F224
Type: Master's thesis
Year: 2008
Downloads: 555
Quote: 6
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Abstract

Key to the success of agricultural futures, the market is the correct analysis, in particular on the market price of the correct forecast. Futures price is the focus of future trace, and it is advanced reflect on the future relationship between supply and demand, the profit and loss situation in the futures market is on the price of the judge to decide by traders. As a solution to China’s "Gengzhe-benefit" social problems the major carriers, agricultural futures markets provide a more accurate long-term price guide information to agricultural production operators, which can reduce the production of blindness. Therefore, analysis and prediction of agricultural futures prices is of great significance.Futures market is a complex nonlinear dynamic system. At present, domestic and foreign research methods for futures price forecasts are much more based component analysis (PCA), the moving average, exponential smoothing, and other traditional economic forecasting methods, and error back-propagation neural network (BP), Fuzzy neural networks, probability Neural networks, and other artificial neural network method. In practical using, the traditional single prediction method is simple, but the accuracy of prediction is not too high. The use of traditional time-series forecasting methods can not reveal its internal law.Based on the analysis of the traditional inspection methods of predicting and analyzing, proposed on the basis of an analysis of the futures market for agricultural products, forecast by the neural network and principal component analysis of the composition of the new forecasting method, and new methods for the performance improvements and enhancements to the in-depth study. This article will introduce the principal component analysis method to futures market forecast, the original input variables pretreatment, select input variables as the principal component network input, on the one hand to reduce the importation of dimensions, eliminating all of the relevant input variables; on the other hand increase the neural network convergence and stability, but also to simplify the structure of the neural network.Since many users lack the necessary neural network, or even computer programming knowledge, we use Matlab language neural network toolbox, a principal component design -neural network model system. The model system can provide a powerful computing capability to the user, and allows users customize their neural network model according to their needs easily. At the same time, the core of the neural network algorithm BP algorithm is proposed to improve the three improved algorithm, namely the momentum and learning rate adjustment since the gradient descent algorithm, adaptive learning rate gradient descent algorithm and lead in the gradient descent algorithm, and Three algorithms are given a concrete realization of the data confirmed that three optimization algorithms in predicting performance better performance.Based on the design of the model, we provide the agricultural futures of the two typical examples of specific design applications as a reference, while the other major agricultural products are a principal component neural network, and further that the main components of the neural network scalability. Confirmed through examples based on principal component of neural network used in agricultural futures markets predict the feasibility and accuracy, also told users of the model’s basic ideas and raising capital gains of investors in futures provide a favorable basis for the theory. Artificial neural network algorithm is applied to the BP futures forecast is for BP to expand the field of application of algorithms, in the financial sector to have a better value.

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