Thispaperutilize diffeffdistant observation data of suzhou huqiu tower distortion to found regress model and arimamodel , theresultisrelativelysatisfaction 本文对苏州虎丘塔变形的不等间隔观测数据,建立了回归模型和arima模型,结果比较满意。
If the datum are less than 50 , that is to say , datum of necessary modeling are insufficient , model is often relatively poor precision by arima model 如果数据少于50个,即所需建模的数据不充分,由arima模型法得到的预测模型往往精度比较差。
Hence , by observing certain characteristics , an optimal fitting model can be selected from a prior modes family , such as arima models , regression models , threshold models , and so forth 传统的预测方法一般是根据实际观测的统计资料去拟合各种先验的模型如arima模型、回归模型等,根据其实际拟合情况,找出最合适的模型。
Arima model and dynamic pca or pls methods have been employed to deal with the non - stationary issues and made a good progress . however , there are severe limitations about those dynamic models Arima模型以及动态pca或pls方法已经应用于非平稳工程系统状态监测与故障诊断领域,但是,这些方法在非平稳系统模型中存在许多局限性。
Interesting results gained in the paper : brief review of development of shenzhen stock market in the last 10 years with its component index shows the market has experienced course from the initial to related mature 本文得到了如下有意义的结果:依据对深圳股票市场的股票成分指数的变化分析,对深圳股票市场的发展进行了简要回顾,建立了arima模型并进行了预测。
ARIMA模型(Autoregressive Integrated Moving Average model),差分自回归滑动平均模型(滑动也译作移动),又称求合自回归滑动平均模型,时间序列预测分析方法之一。ARIMA(p,d,q)中,AR是"自回归",p为自回归项数;MA为"滑动平均",q为滑动平均项数,d为使之成为平稳序列所做的差分次数(阶数)。