Time Series Model Error Analysis And Research

With the development of economy and the conversion of people's investment consciousness, the people's investing enthusiasm of stock is higher and higher.Stock investment becomes an important part of economy investment, and also an important part of people's life in modern times. In the face of the fast changing stock market, investors will face a lot of uncertain factors while carrying on investing in stock market. Through researches into its internal disciplinarian, effective analytic methods and tools are being looked for more interest with lower risk. Therefore the study of disciplinarian in the model error of financial time series has great theoretical significant and applicable value.Modeling methods are generally used in fitting security time series. In the traditional time series analyzing methods, the fitting results are effective in clear tendency, but some fitting errors are relatively great. This paper uses the time series analyzing methods to build the model for two different stocks, deeply analyze and research on the stock fitting error and unusual error. The analysis conclusions as follows:on one hand, the unusual errors of two different time series model almost have the same time point; on another hand, the errors fluctuate with almost a same step. It shows when we analyze the stock price series with the time series model, the model error size, the model error changing situation, and the production of unusual error are irrelevant with the size of analytic target.In this paper, the analyze data come from the close-price in one hundred and twenty trading days of Jinmu stock (601958) and Chubanchuanmei stock (601999).After analyze the model error, model unusual error and the price changing rate, the result shows that the influence to stock's time series model error mainly produced by the influence of external factors act on the intensity of the attribute of analytic target.The key point of model fitting result doesn't lie in the target's own attributive character, but in the influence that external factors act on. The external factors influence the attributive character's changing direction and intensity, it is shown as the direction of price rising or reducing, and amount of price rising or reducing. This conclusion will be guidance and practicality in theory analyzing for time series model in future. In this paper, a suitable method for detecting the unusual error in time series model is researched, and it's an improved method than the traditional analysis of the outlier detection.