R 如何创建根据年龄调整的时间序列？

我有过去13个月的住院数据。我的数据包含变量/列：ID、年龄、入院日期、出院日期、住院时间、年龄组（从0:4开始的5年增量）、按年龄组划分的平均住院时间、按入院日期划分的平均住院时间

我已经创建了ACF和PACF图：

    plot(hospitalized$`Hospital Admission Date`, hospitalized$avg_stay_pDate,type = "l",
         ylab = "Days Spent in Hospital",xlab = "Date Admitted",cex.lab = 1.4)
    
    ##plot ACF of data
    
    forecast::Acf(hospitalized$avg_stay_pDate, main = "",cex.lab = 1.4,
                  lag.max = 30, ylab = "ACF")
    
    ##plot partial ACF
    forecast::Acf(hospitalized$avg_stay_pDate,main = "",cex.lab = 1.4,
                  lag.max = 30,ylab = "PACF",
                  type = "partial")```

Which results in AR(5) and MA(3), although admittedly I don't really know what I'm talking about here. I just know that's where the corresponding plots become insignificant. 

I compared AIC and BIC for AR, MA, ARMA, ARIMA, and SARIMA models and SARIMA had the lowest numbers so I chose SARIMA. 


    ```## Find the best model
    BIC(lm_mod)
    ar1_simp$bic
    ma1_simp$bic
    arma1_simp$bic
    arima1_simp$bic
    sarima1_simp$bic
    
    
    ## Find the best model
    AIC(lm_mod)
    ar1_simp$aic
    ma1_simp$aic
    arma1_simp$aic
    arima1_simp$aic
    sarima1_simp$aic```

Output:
[1] 5701.889
[1] 5149.502
[1] 5149.502
[1] 5149.502
[1] 5143.878
[1] 5082.838

[1] 5688.029
[1] 5103.301
[1] 5103.301
[1] 5103.301
[1] 5102.309
[1] 5032.39

Then I tried to use my model for prediction:

    ```## Subset the data
    
    hosp.test = hosp_ts[563:750]
    hosp.train = hosp_ts[1:562]
    
    ## Fit model to subset off the data
    
    train.mod = forecast::Arima(hosp.train,
                                order = c(5, 1, 3),
                                seasonal = list(order = c(5,1,3),period = 40))
    
    ## calculate errors/bias
    ## Forecast
    #h = steps ahead
    
    pred = forecast::forecast(train.mod,h = 188)
    
    ## plot predictions
    
    plot(pred,xlab = "Week",ylab = "Google Index Score",cex.lab = 1.4,
         ylim = c(0,45),xlim = c(0,750))
    
    
    sqrt(mean((hosp.test - pred$mean)^2)) ### RPMSE
    mean(pred$mean - hosp.test)           ### bias
    
    sd(hospitalized$avg_stay_pDate)```

my RPMSE is 3.97, my bias is -2.192, and my sd is 10.72. I don't think my model is bad (except I can't figure out how big to make my period. After looking at the graph I thought maybe 60, but Rstudio said it wouldn't let me create an 8gb vector HA!) but I need to create a predictive time series that is adjusted for age and I don't really see how to do that.