I have a time Series data like
patients: 100, 200, 300,...,10, 5,...,120 Month: 1, 2 , 3 ..., 12, 1,...,12 Year: 2006,2006,2006,...,2006,2007,..,2007
Like this I have data till 2010, I need to predict patients for 2011
I wanted to check for the trend in the data, so I used below ggplot to plot the trend line. When I used only "geom_smooth()" I got a exponential curve line based on loose() and when I use geom_smooth(method="lm"). I got a straight line. My question is which one is correct?.
A. I have TS and from both the graphs I see I only have a trend and no seasonality(which is correct). For fitting TS data which one is correct?
ggplot(data,aes(x= Year,y=Incoming.Examinations))+ geom_point(aes(color=Month))+ geom_smooth() #geom_smooth(method="lm") imputed<- mice(data) imputed$imp fit <- with(imputed,ets(Incoming.Examinations,model="AAN")) #fit <- pool(fit) fit completedData<-complete(imputed,4) fit<-ets(completedData$ts,model="AAN") y<-predict(fit,n.ahead=15)
B. I had missing values in the "patients",so, I used mice package to impute the missing values. As I only have trend, I decided to use Double exponential smoothing in
using ets(). Now, the problem is
fit <- pool(fit)
gives me an error
Error: No glance method for objects of class ets
looks like pool works only with lm, glm in with(). B.1 How do I fix this?
B.2 As a work around,I dumped fit and checked all the results and picked the dataset 4 with low AIC and predicted the output. is this right thing to do? or is it mandatory to use the pool(fit) and then make a decision?