The Gray Bootstrap Model Based On Genetic Algorithms

With the development of China’s reform and opening up, socialist economy isgetting better, the real estate industry also presents a thriving scene. But the real estatespeculation have emerged, the rapid growth in house price is far beyond the purchasingpower of the masses, the bubble economy in the property market has seriously affectedthe healthy development of the national economy.State and government began to macro-control on the house price in2010, so far,China has had the support of real estate policy shift to curb speculation, to curb houseprice, and they also has taken a variety of control solutions, for example, land, financeand tax. Now, the house price have curbed. In this paper, because the house price inChongqing became effective in2011, so we can use the data to forecast the house pricein Chongqing after that. However, it is still a relatively difficult problem that how toestablish a reliable prediction model to predict the effective house price.Due to the small amount of quarterly data since2011, so we cannot use the usualclassical large sample forecasting methods to predict it. In this paper, firstly, we can use away that it simulate an unknown distribution, and it is a statistical sampling methods,then we call it bootstrap. We use this way to sample the original data to increase the thesample size. Then bootstrap is combined with gray prediction model, and this new wayis called bootstrap gray model. We can use it to predict small sample data. Finally, weintroduce a model with a parameter, and we use the Genetic Algorithms to correct theparameter, then we can obtain a model that it minimize the global error. We call it thegray bootstrap model based on Genetic Algorithms. We only obtain a forecast range bythe gray model and the gray bootstrap model based on Genetic Algorithms, however, theforecast range is large, then it is proved to be correct, but we cannot obtain specificvalues. For the consumers, the specific values is very important. In this paper, weintroduce a new selective sequence, then we can predict the future house price by thelast selective sequence code.