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Influenza is a recurring infectious diseases, caused a high incidence and high mortality rates in the world. Influenza viruses are divided into three categories: Influenza A (A-type), beta (B type), hepatitis C (C-type). three types of influenza virus is the most deadly flu virus to humans has brought serious disease In 2009 influenza virus pandemic broke out again, and the human experience in the 20th century, several times the outbreak of the influenza virus, have shown that our understanding of the influenza virus is not comprehensive, and many of their features to be further excavation. influenza virus great threat to human health, so further study of DNA sequences and protein sequences of influenza virus is an imminent work, their characteristics on the prevention of influenza virus, the new vaccine, drug design, control and treatment of important in bioinformatics research background, the article describes the main method to study the characteristics of biological sequence time series theory methods. mainly through the processing of dynamic data, analysis, forecasting and control to use this article ARIMA (p, d, q) model and ARFIMA (p, d, q) model defined the nature and method made elaborate the theoretical preparations made for the study of influenza virus DNA sequences and protein sequences characteristic. based CGR coordinates the DNA sequence of the influenza virus convert the CGR radians sequence, and the introduction of long memory model ARFIMA model to analyze. discovered 10 randomly recruited from the DNA sequence of the influenza virus H1N1 sequences and 10 H3N2 sequences have long correlation and fit very well, and also found that these two sequences can try to identify different ARFIMA model, which H1N1 available ARFIMA (0, d, 5) model to identify, H3N2 can ARFIMA (1, d, 1) model to identify the DNA sequence of the beta, gamma influenza virus analysis found randomly recruited The 10 beta sequence and 10 HCV sequences Similarly long correlation and fitting well, and also found that these two sequences can also try different ARFIMA model to identify as an algorithm has perfect classic time series models, ARFIMA model can help us dig unknown characteristics in the DNA sequence of the influenza virus. ARIMA model to predict influenza virus subtype H1N1 DNA sequence of bases, and this has an important significance of the H1N1 virus research we selected 1970 -2010 relatively high endogenous 41 HINI influenza virus data, ARIMA (p, d, q) model fitting and prediction, the first 20 positions by the forecast area a few exceptions display the raw data in the forecast area, indicating reasonable model forecast good effect. Based on this, the same way of the H1N1 subtype of influenza virus hemagglutinin amino acid sequence analysis also found that the forecast good effect.
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