The Application of Auto Regressive Time Varying Models to the Network Building of Time Series Microarray Data / 中国卫生统计

Objective To explore the application of auto regressive time varying models in network building of time se-ries microarray data.Methods We used actual data to carry out a preliminary discussion about the properties of auto regressive time varying models.Results Analysis results of actual data suggested that auto regressive time varying models can perform well whether the number of timepoint is large or small,and it can recognize the network’s dynamic variation rule.Conclusion Auto regressive time varying models is applicable to network building of time series microarray data.

Research on electroencephalogram emotion recognition based on the feature fusion algorithm of auto regressive model and wavelet packet entropy / 生物医学工程学杂志

Focused on the world-wide issue of improving the accuracy of emotion recognition, this paper proposes an electroencephalogram (EEG) signal feature extraction algorithm based on wavelet packet energy entropy and auto-regressive (AR) model. The auto-regressive process can be approached to EEG signal as much as possible, and provide a wealth of spectral information with few parameters. The wavelet packet entropy reflects the spectral energy distribution of the signal in each frequency band. Combination of them gives a better reflect of the energy characteristics of EEG signals. Feature extraction and fusion are implemented based on kernel principal component analysis. Six emotional states from a public multimodal database for emotion analysis using physiological signals (DEAP) are recognized. The results show that the recognition accuracy of the proposed algorithm is more than 90%, and the highest recognition accuracy is 99.33%. It indicates that this algorithm can extract the feature of EEG emotion well, and it is a kind of effective emotion feature extraction algorithm, providing support to emotion recognition.

Application of Seasonal Auto-regressive Integrated Moving Average Model in Forecasting the Incidence of Hand-foot-mouth Disease in Wuhan, China / 华中科技大学学报（医学）（英德文版）

Outbreaks of hand-foot-mouth disease (HFMD) have occurred many times and caused serious health burden in China since 2008.Application of modem information technology to prediction and early response can be helpful for efficient HFMD prevention and control.A seasonal auto-regressive integrated moving average (ARIMA) model for time series analysis was designed in this study.Eighty-four-month (from January 2009 to December 2015) retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling.The coefficient of determination (R2),normalized Bayesian Information Criterion (BIC) and Q-test P value were used to evaluate the goodness-of-fit of constructed models.Subsequently,the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016.The best-fitted seasonal ARIMA model was identified as (1,0,1)(0,1,1)12,with the largest coefficient of determination (R2=0.743) and lowest normalized BIC (BIC=3.645) value.The residuals of the model also showed non-significant autocorrelations (PBox-Ljung (Q)=0.299).The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval,including a major peak during April to June,and again a light peak for September to November.The ARIMA model proposed in this study can forecast HFMD incidence trend effectively,which could provide useful support for future HFMD prevention and control in the study area.Besides,further observations should be added continually into the modeling data set,and parameters of the models should be adjusted accordingly.

Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model / 中国血吸虫病防治杂志

Objective To explore the effect of the autoregressive integrated moving average model?nonlinear auto?regressive neural network(ARIMA?NARNN)model on predicting schistosomiasis infection rates of population. Methods The ARIMA model,NARNN model and ARIMA?NARNN model were established based on monthly schistosomiasis infection rates from Janu?ary 2005 to February 2015 in Jiangsu Province,China. The fitting and prediction performances of the three models were com?pared. Results Compared to the ARIMA model and NARNN model,the mean square error(MSE),mean absolute error (MAE)and mean absolute percentage error(MAPE)of the ARIMA?NARNN model were the least with the values of 0.011 1, 0.090 0 and 0.282 4,respectively. Conclusion The ARIMA?NARNN model could effectively fit and predict schistosomiasis in?fection rates of population,which might have a great application value for the prevention and control of schistosomiasis.

Modeling and forecasting of the under-five mortality rate in Kermanshah province in Iran: a time series analysis / 한국역학회지

OBJECTIVES: The target of the Fourth Millennium Development Goal (MDG-4) is to reduce the rate of under-five mortality by two-thirds between 1990 and 2015. Despite substantial progress towards achieving the target of the MDG-4 in Iran at the national level, differences at the sub-national levels should be taken into consideration. METHODS: The under-five mortality data available from the Deputy of Public Health, Kermanshah University of Medical Sciences, was used in order to perform a time series analysis of the monthly under-five mortality rate (U5MR) from 2005 to 2012 in Kermanshah province in the west of Iran. After primary analysis, a seasonal auto-regressive integrated moving average model was chosen as the best fitting model based on model selection criteria. RESULTS: The model was assessed and proved to be adequate in describing variations in the data. However, the unexpected presence of a stochastic increasing trend and a seasonal component with a periodicity of six months in the fitted model are very likely to be consequences of poor quality of data collection and reporting systems. CONCLUSIONS: The present work is the first attempt at time series modeling of the U5MR in Iran, and reveals that improvement of under-five mortality data collection in health facilities and their corresponding systems is a major challenge to fully achieving the MGD-4 in Iran. Studies similar to the present work can enhance the understanding of the invisible patterns in U5MR, monitor progress towards the MGD-4, and predict the impact of future variations on the U5MR.

Autoregressive integrated moving average model and circle distribution analysis of stroke mortality in Tianjin / 中华流行病学杂志

To develop a model for forecasting the mortality of stroke in Tianjin,China.The time series of stroke mortality from 1999 Jan.to 2006 Dec.in Tianjin city were subjected.Circle distribution analysis was used to verify the trend of time concentration.Multiple seasonal autoregressive integrated moving average model [ARIMA (p,d,q) (P,D,Q)s],based on model identification,estimation and verification of parameter,and analysis of the fitting of model,was established.Most of the deaths from stroke occurred in January and had a cycle of 12 months.An AR/MA model (0,1,0)×(0,1,1)12 was established(1-B)(1-B12) lnxt=0.001+(1-0.537 B12)εt.Conclusion: ARIMA & Circle Distribution analysis is an important tool for stroke mortality analysis.Potentially it has a high practical value on the surveillance,forecasting and prevention of stroke mortality.

Comparison of predictive effect between the single auto regressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) combination model on the incidence of scarlet fever / 中华流行病学杂志

R2) of the two models were 0.801,0.872 respectively. The fitting efficacy of the ARIMA-GRNN combination model was better than the single ARIMA, which had practical value in the research on time series data such as the incidence of scarlet fever.

The Application of the Prediction of the Reported Weekly Incidence of Bacillary Dysentery in Chaoyang District Using the Time Series Model / 中国卫生统计

Objective The study estabfished a model to pre-dict the weekly incidence of bacillary dysentery in Chaoyang District,and evaluated its predictive effects. Methods To eliminate the factors of sea-son-changing by means of Time Series. Auto regressive integrated moving average(ARIMA), based on model identification, estimation andverifica-tion of parameter, and analysis of the fitting of model, was established. Fi-nally,the predictive model was established by the multiple of ARLMA and seasonal factors. Results The error of the model for the prediction was -0.06 on average. The relative error was 2.32% on average. Conclusion Time series could not only accurately predict useing the data which was collected every week,but shorten the cycle of prediction.

Modelo logístico difásico no estudo do crescimento de fêmeas da raça Hereford / Difasics logistic model in the study of the growth of Hereford breed females

Este trabalho teve como objetivo comparar modelos logísticos difásicos ponderados aplicados ao estudo de curvas de crescimento de fêmeas Hereford com três diferentes estruturas de erros: erros independentes (EI), auto-regressivos de primeira ordem (AR (1)) e auto-regressivo de segunda ordem (AR (2)) a dados de peso-idade de 55 fêmeas da raça Hereford avaliadas desde o nascimento até 675 dias de idade. Utilizou-se o procedimento model do software Statistical Analysis System (SAS) por meio das opções weight e por centoAR. A comparação entre os modelos foi realizada com base na interpretação biológica dos parâmetros e nos avaliadores de qualidade de ajuste (coeficiente de determinação ajustado, teste de Durbin-Watson, desvio padrão residual, número de iterações), além do critério de informação de Akaike (AIC) e do teste F para comparação de modelos. Os resultados obtidos para o ajuste dos modelos aos dados médios indicaram que o modelo logístico difásico AR (2) foi o mais eficiente para descrever a curva de crescimento do rebanho. Ao se considerar o conjunto de dados individuais, nenhum dos modelos abordados foi recomendado por produzirem estimativas não condizentes com a realidade.

This study had the objective of comparing weighted difasics logistic models applied to the study of Hereford females growth curves with three different error structures: independent errors (IE), first-order auto-regressive (AR (1)) and second-order auto-regressive (AR (2)) to weight-age data of 55 females of the Hereford breed, raised in the Bagé region, RS, Brazil, evaluated from birth to 675 days old. The weight and percentAR options of model procedure, available in the software Statistical Analysis System (SAS), was used to fit data. The comparison among the models was carried out through the biological interpretation basis of the parameters and in the adjustment of quality measures (adjusted determination coefficient, Durbin-Watson test, residual standard desviation, number of iterations), beyond the Akaike information criteria (AIC) and the F test for model comparison. The models fitted to mean data indicated that the difasic logistic with AR(2) structure was the most efficient to describe the herd growth curve. In the individual fit, none of the models was accepted because they didn't produce consistent estimates.

Study on Interaction between the Price of 7-Amino-cephalsporanic Acid and the Price of Its Preparation Cefazolin Sodium / 中国药房

OBJECTIVE:To probe into the interaction between the price of 7-amino-cephalsoranic acid(7-ACA) and the price of its preparation(cefazolin sodium) and to provide reference for price strategy of pharmaceutical enterprises.METHODS:Correlation analysis,autocorrelation analysis and auto-regressive distributed lag were applied to analyze the samples which were collected from monthly selling price of 7-ACA of China Shijiazhuang Pharmaceutical Group Co.,Ltd.during 24 months.RESULTS:There was positive correlation between the price of 7-ACA and the price of cefazolin sodium with correlation coefficient of 0.682 5.The price of 7-ACA and cefazolin sodium are autocorrelative respectively.The price of 7-ACA was influenced by previous price,besides raw material cost,production cost and selling expenditures.The price of 7-ACA changed 3 months later because of previous price.The price of 7-ACA would change 2 months later as the price of cefazolin sodium changed and it changed 1 month later as previous price changed.When the price of 7-ACA changed,the price of cefazolin sodium price would change one month later.CONCLUSION:Effective price strategy should be decided according to the interaction between the price of crude drug and the price of preparation besides the cost of raw materials,production costs and selling expenditures.

Applications of ARIMA model on predictive incidence of influenza / 第三军医大学学报

Objective To explore the application of auto regressive integrated moving average (ARIMA) and establish a predictive model for influenza to forecast the dynamic trend in order to develop the prevention policy scientifically. Methods Samples which caught influenza from 2002 Jan to 2006 Jun in Chongqing city were subjected. SPSS was used to fit ARIMA model,and Q statistic was used to verify the applicability of the model. Results The model of ARIMA(1,1,1) was established. The statistic of Q was smaller than ?2_?(m), verifying the applicability of this model. Conclusion The ARIMA model can be used to analyze the influenza incidence and make a short-term prediction.