西南边境作战某军减员率的ARIMA分析

为研究战役卫生减员的统计分布,建立模型,提供计算机模拟研究的基础。本文收集了我军西南边境作战某军的经验数据,计算每天减员率,采用柯尔莫哥洛夫—斯米尔诺夫方法进行拟合优度检验,运用博克斯 詹金斯方法建立时间序列模型。结果表明该军减员率分布符合对数正态分布,可用ARIMA(0,1,1)建模。

中医院门急诊量ARIMA分析与对策研究

目的:利用ARIMA模型来探讨分析中医院门急诊量的具体情况,以增减中医院门急诊储备及预测中医院未来的门急诊量,为中医院门急诊量管理提供参考。方法:选取中医院2019~2020年两年内的每月的门急诊量数据作为一般资料,并采用统计学方法对所搜集的数据进行处理,预测中医院未来3年的门急诊量。结果:该模型具有明显的趋势性特征以及季节性成分,不存在预测变量数,模型拟合统计量也比较平稳。2019~2020年的每月门急诊量的ARIMA模型的预测值和实际值的相对误差都较小,这说明2019~2020年的每月门急诊量的ARIMA模型的预测值和实际值几乎相同,模型吻合,趋势相似。2023年的门急诊量预测值最大,高达561 902人次;其次是2022年,数值为518 441人次;最后是2021年,数值是476 877人次。结论:ARIMA模型的预测效果很好,误差小,拟合度高。中医院2021~2023年这三年门急诊量预测也很高,呈现出季节性成分。当然,中医院也应当采取积极的措施,增加门急诊量,发挥中医特色。

基于ARIMA模型的大坝滑坡体表面位移监测分析预报

大坝滑坡体变形监测是水电站施工建设和分期蓄水阶段安全性的重要保障,监测数据分析和处理的方法又会直接影响到变形预报的质量。以某水电站坝址下游滑坡体为例,应用时间序列分析法中的ARIMA模型,构建非平稳时间序列模型对表面监测数据进行分析处理,比较模型预测数据与实际位移数据之间的差异。试验表明,ARIMA模型在滑坡体表面位移分析和预报方面具有较高的精度和可靠性。

突发公共卫生事件对浙江省餐饮企业影响的实证分析——以新冠疫情为例

本文旨在通过实证分析,回顾新冠疫情对浙江省餐饮企业的影响,探讨公共卫生事件对餐饮业的影响机制和应对策略。本文运用描述性统计分析和时间序列ARIMA干预分析模型,研究2016—2022年浙江省餐饮业平均营业额数据,定量分析新冠疫情对浙江省餐饮企业的影响并评估影响程度。基于此,本文对餐饮企业在预案制定、人员管理、政府推动等方面提出科学、有效的应对建议,以期为浙江省餐饮企业及相关部门提供有益的参考和指导,帮助餐饮企业更好地应对未来可能发生的公共卫生事件,并促进餐饮业的恢复与发展。

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.

平罗县新型农村合作医疗总体费用状况分析

目的了解宁夏平罗县新型农村合作医疗总体费用状况并对费用进行短期预测,为新型农村合作医疗资金的有效使用及合理地调整费用补偿比例提供理论依据。方法收集平罗县2004年1月-2007年2月新型农村合作医疗费用医政报表资料,采用SPSS11.5软件统计描述、ARIMA时间序列分析。结果(1)新型农村合作医疗总筹资费用、住院费用、补偿费用均在逐年增加;(2)2007年新型农村合作医疗住院补偿费用将达到1031.30万元,补偿人数将为11038人。结论宁夏平罗县新型农村合作医疗资金使用率高,农民受益广,但保障水平低,应控制住院费用,提高筹资额度,加强乡级医院建设。

基于时间序列及回归分析模型的物流量预测实证研究

本文运用spss19中的指数平滑模型、ARIMA模型、指数模型和回归分析模型对四川省内江市历年物流量和经济数据进行建模和预测。对四种方法的预测结果进行比较、分析、检验,最后得到内江市未来的物流量最有可能的发展情况,给出平均误差,并针对物流产业的发展提出相关对策。

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.

广西壮族自治区艾滋病模型及预测分析

本文收集了2013年1月—2019年10月广西壮族自治区艾滋病确诊数及死亡率的月度数据,经过对数据的预处理、模型识别、参数估计、模型诊断和优化等分析手段,建立了相应的时间序列模型。通过建立的最优模型对广西壮族自治区艾滋病确诊数及死亡率进行了6期预测分析,并结合结论提出防控建议。结果表明,艾滋病确诊数的最优模型为ARIMA(2,1,2);死亡率可分解为线性趋势项与满足ARIMA(2,1,1)模型的随即波动项之和。利用所建模型,得出未来6个月确诊数及死亡率的预测,并根据预测结果,提出了防控建议。

新常态下四川省产业结构的分析预测

纵观四川省产业结构整体情况,其总体趋势呈"一减,二增,三增"之态。在新常态的作用下,产业结构俨然发生着极富特点的变化。此处通过多元线性回归分析三大产业结构增长率与GDP增长率的关系,运用ARIMA模型基于1996年至2016年的数据,对2017年至2020年的数据进行合理预测。通过预测得知,三大产业增长率及GDP增长率将一如既往遵循新常态的发展特点,实现稳中有进的地区特色科学发展,助力国家在2020年之际,贯彻全面建设成为小康社会的重大举措。

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.

城市经济与环境协调发展度预测研究——以重庆市为例

准确、全面、系统地预测城市经济与环境的协调发展情况是落实科学发展观,实现整个国民经济与社会协调发展战略的重要一环。文章在综合研究近年来重庆市经济与环境发展现状的基础上,选取多维度评价指标体系,基于主成分分析法对重庆市经济与环境的协调发展度进行定量评价,分析其发展原因,并在主成分评价数据的基础上运用ARIMA模型对重庆市经济与环境协调发展度的发展趋势进行预测,并提出对策建议。

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.

基于数学方法的音乐流派影响因素与演变预测研究

音乐作为人类文化遗传的一部分,与人类的生活密不可分。同时,不同的音乐流派也对人类的社会生活有不同影响,研究音乐流派发展就显得十分必要。首先利用灰色关联分析与K-Mean聚类分析对影响音乐流派的因素进行了分析,然后基于已有数据,建立了时间序列ARMA模型,对音乐流派在未来5年的发展趋势进行了预测。

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.

新冠肺炎疫情对餐饮业影响的实证分析——以辽宁省为例

选取2016年1月至2020年6月的辽宁省1500余家限额以上餐饮企业(单位)经营情况数据,运用描述性统计分析方法与时间序列ARIMA干预分析模型,就新冠肺炎疫情对辽宁省餐饮业产生的影响进行定量分析,并测算其影响的程度。进而对餐饮企业在物资储备、方案预案、制度规定、环境整治等方面提出科学、有效的应对建议。