AB分型联合Arima分型对食管表浅鳞状细胞癌浸润深度判定的价值

目的探讨AB分型联合Arima分型预测浅表食管鳞状细胞癌(superficial esophageal squamous cell carcinomas,SESCC)浸润深度的临床价值。方法2017年7月至2022年12月期间在宁波市医疗中心李惠利医院及江苏省中医院接受内镜黏膜下剥离术治疗且鳞状上皮乳头内毛细血管袢(intra-epithelial papillary capillary loops,IPCL)AB分型为B2型的患者(76处SESCC病变)纳入回顾性研究,根据Arima分型规则对IPCL进行二次分类,以病理判定的浸润深度为金标准,分析B2型联合Arima分型对SESCC浸润深度预测的敏感度、特异度、阳性预测值和阴性预测值。结果76处病变中,31处(40.79%)浸润黏膜肌层(T1a-MM)或黏膜下层浅层(T1b-SM1),B2型IPCL预测T1a-MM/T1b-SM1 SESCC的敏感度、特异度、阳性预测值、阴性预测值、准确率分别为70.45%(31/44)、79.64%(176/221)、40.79%(31/76)、93.12%(176/189)、78.11%(207/265)。对病变的IPCL进行Arima分型后,B2-4ML型、B2-AVA-4M型IPCL预测T1a-MM/T1b-SM1 SESCC的敏感度、特异度、阳性预测值、阴性预测值、准确率分别为61.36%(27/44)、88.24%(195/221)、50.94%(27/53)、91.98%(195/212)、83.77%(222/265)和38.64%(17/44)、94.57%(209/221)、58.62%(17/29)、88.56%(209/236)、85.28%(226/265)。结论B2型IPCL联合Arima分型可以提高对T1a-MM/T1b-SM1 SESCC的诊断准确性。

Prediction and Analysis of O_3 based on the ARIMA Model

The research conducted prediction on changes of atmosphere pollution during July 9, 2014-July 22, 2014 with SPSS based on monitored data of O3 in 13 successive weeks from 6 sites in Baoding City and demonstrated prediction effect of ARIMA model is good by Ljung-Box Q-test and R2, and the model can be used for prediction on future atmosphere pollutant changes.

Time-series analysis with a hybrid Box-Jenkins ARIMA

Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.

Annual Earnings Analysis with ARIMA for Future Earnings Prediction

This study investigates annual earnings analysis with ARIMA （Autoregressive Integrated Moving Average） for future earnings prediction. Earnings prediction is very important to be used in various aspect of decision making process, such as： investor, creditor, analyst, academicians, practitioners, etc.. Evidence supports the ARIMA model that it is more accurate. It also has a smaller size of error value.

Estimation of Number Of Small Cattle Through ARIMA Models in Turkey

In this study, the number of sheep and goats in Turkey were analysed by time series analysis method, and the number of great cattle for next years predicted through the most appropriate time series model.Time series was formed using the data on the number of sheep and goats belonging to the period between 1930 and 2014 in Turkey It was determined through autocorrelation function graphic that the series weren＇t stationary at first, but they became stationary after their first difference were calculated. A stagnancy test was performed through extended Dickey-Fuller test. So as to determine the suitability of the model, it was reviewed if autocorrelation and partial autocorrelation graphs were white noise series and also the results of Box-Ljung test were reviwed. Through the ＂tested models, the model estimations, of which parameter estimates were significant and Akaike information criterion （AIC） was the smallest, were performed. The most appropriate model in terms of both the number of sheep and goats is first-level integrated moving average model stated as ARIMA（0,1,1）. In this model, it was estimated that there would be an increase in the number of sheep and goats in Turkey between the years of 2015 and 2020, however, the increase in the number of sheep would be more than the increase in the number of goats.

Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.

An Empirical Study on the stock Price of Modeling

the model in time series analysis are widely used in the field of economy. We often use the model in time series to analyze data, but without regard to the rationality of the model. In this paper, we introduce and analyze Ping An Of China（601318） shares at the opening price（2013/01/04-2013/07/04）.The model is established by analyzing data. Modeling steps of ARIMA model and GARCH model are presented in this paper. The data whether ARIMA model is suitable by white noise. Or the data whether GARCH model is suitable by since the correlation of variance test. By comparing the analysis, it selects a more reasonable model.