The traditional time series analysis method divides the fluctuation of time series into four factors: long-term trend change factor (T), seasonal change factor (S), business cycle change factor (C) and irregular change factor (I). When Census X12-GM(1,1) method is applied to other types of time series, the calculation formula of the empirical seasonal factor may be different. In the process of forecasting monthly pork price time series using Census X12-GM(1,1) method, a calculation parameter called empirical seasonal factor was constructed.
By comparing the prediction results of this method with other models, it was proved that X12-GM(1,1) model combination method has higher prediction accuracy. In this paper, a new model combination method based on Census X12 model and GM (1,1) model was used to fit and forecast monthly pork price series.
At the same time, in order to further improve the prediction accuracy, GM (1,1) model is also combined with other models. GM (1,1) model can be used to predict a wide range of time series, such as traffic data prediction, financial data prediction, agricultural data prediction, weather data, geological disaster data, disease prevention and control data, etc. Its characteristic is that it can use a small amount of data to model and predict the series data. It describes the dynamic changes of time series by establishing a first-order linear ordinary differential equation. It is one of the core contents of the grey system theory established by Professor Deng Julong in 1982. GM (1,1) model is one of the most important models for time series prediction. In order to improve the accuracy of time series prediction, multiple model combination method is also widely used. In addition, in order to analyze the factors that affect the change of time series, Census X12 model is used to decompose the seasonal and long-term change trend of time series. With the development of artificial intelligence technology, various kinds of neural network technology are also used to forecast time series. The models commonly used to fit and forecast time series include ARIMA model, grey system model, Holt winters exponential smoothing model and so on. The monthly pork price data are a kind of typical time series. Nowadays, there are many mathematical models that can be used to fit and predict time series data. It takes about six months for a pig to become a marketable pork commodity from birth, that is to say, the price changes in the next six months are most meaningful for stakeholders. For example, farmers can decide whether to expand the scale, middlemen can decide whether to increase or reduce the quantity of pork stocks, and final consumers may also consider whether to replace pork with other food. Understanding the change of pork price in advance can also make farmers, middlemen and final consumers prepare for the follow-up plan.
Through the analysis of pork price trend, government departments can formulate strategies according to local conditions, such as encouraging breeding, putting government reserve pork into the market, or taking restrictive measures. The stability and controllability of pork price is not only related to the living standard of ordinary people, but also reflects the level of national governance in a sense. The rising price of pork in 2019 is also an important factor leading to the continuous rise of China CPI. Although pig breeding enterprises and individuals have also applied some innovative technologies to expand the production of pigs, the imbalance between supply and demand of pork is still an important factor affecting the price of pork in China. According to statistics, China needs about 54 million tons of pork every year, but the data released by National Bureau of statistics(Mainland China), pork output in 2019 is only 42.55 million tons, which shows that there is still a big gap between this quantity and the actual demand. In many country, pork is one of the daily necessities for most ordinary families.