Chinese chemical fertilizer price index prediction method based on BP neural network

Abstract

The invention relates to a prediction method and particularly relates to a Chinese chemical fertilizer price index prediction method based on a BP neural network, belonging to the technical field of time sequence prediction. According to the technical scheme provided by the invention, the Chinese chemical fertilizer price index prediction method based on a BP neural network comprises the following steps: (a) training the BP neural network by use of the historical data of the Chinese chemical fertilizer price to obtain a price index prediction model; and (b) predicting the price index by use of the price index prediction model. The method provided by the invention needs consideration of statistical property calculation and can be theoretically applied to the modeling of any non-linear time sequence; with a unique non-traditional expression way and inherent learning ability, the method has tremendous advantages in controlling highly non-linear and seriously uncertain systems; and the method realizes high accuracy of predicting the Chinese chemical fertilizer price index.

Description

technical field [0001] The invention relates to a forecasting method, in particular to a forecasting method of China's chemical fertilizer price index based on BP neural network, which belongs to the technical field of time series forecasting. Background technique [0002] Chemical fertilizers occupy an important position in my country's agriculture. The correct prediction of China's chemical fertilizer price index can not only be used as the basis for enterprises and farmers to make production decisions, so that they can take the initiative and win benefits for them, but also provide scientific information for the government to formulate relevant policies. In order to improve the effective allocation of agricultural resources and promote the sustainable and healthy development of agriculture. [0003] Time series prediction can be divided into simple sequential average method, weighted sequential average method, simple moving average method, weighted moving average method, e...

AI Technical Summary

Problems solved by technology

Traditional time series forecasting methods are generally only suitable for linear models or some nonlinear models that can be linearized, or the estimation of parameters is very difficult, not easy to use, and the forecasting effect is not ideal