StackOverflow2013

Note that there are some explanatory texts on larger screens.

plurals

PO
primarykey
Id
15683573
data
AcceptedAnswerId
0
AnswerCount
0
ClosedDate
CommentCount
0
CommunityOwnedDate
CreationDate
2013-03-28T13:47:42.960
FavoriteCount
0
LastActivityDate
2013-03-28T17:16:43.747
LastEditDate
2013-03-28T17:16:43.747
LastEditorUserId
1235661
OwnerUserId
1235661
ParentId
15673962
PostTypeId
2
Score
7
ViewCount
0
LastEditorDisplayName
text
Body
The problem with @ndoogan's answer is that logarithm is not a linear transformation. Which means that E[exp(y)] != exp(E[y]). <a href="http://en.wikipedia.org/wiki/Jensen%27s_inequality">Jensen's inequality</a> gives actually that E[exp(y)] >= exp(E[y]). Here's a simple demonstration: <pre><code>set.seed(1) x<-rnorm(1000) mean(exp(x)) [1] 1.685356 exp(mean(x)) [1] 0.9884194 </code></pre> Here's a case concerning the prediction: <pre><code># Simulate AR(1) process set.seed(1) y<-10+arima.sim(model=list(ar=0.9),n=100) # Fit on logarithmic scale fit<-arima(log(y),c(1,0,0)) #Simulate one step ahead set.seed(123) y_101_log <- fit$coef[2]*(1-fit$coef[1]) + fit$coef[1]*log(y[100]) + rnorm(n=1000,sd=sqrt(fit$sigma2)) y_101<-exp(y_101_log) #transform to natural scale exp(mean(y_101_log)) # This is exp(E(log(y_101))) [1] 5.86717 # Same as exp(predict(fit,n.ahead=1)$pred) # differs bit because simulation mean(y_101) # This is E(exp(log(y_101)))=E(y_101) [1] 5.904633 # 95% Prediction intervals: #Naive way: pred<-predict(fit,n.ahead=1) c(exp(pred$pred-1.96*pred$se),exp(pred$pred+1.96*pred$se)) pred$pred pred$pred 4.762880 7.268523 # Correct ones: quantile(y_101,probs=c(0.025,0.975)) 2.5% 97.5% 4.772363 7.329826 </code></pre> This also provides a solution to your problem in general sense: <ol> <li>Fit your model</li> <li>Simulate multiple samples from that model (for example one step ahead predictions as above)</li> <li>For each simulated sample, make the inverse transformation to get the values in original scale</li> <li>From these simulated samples you can compute the expected value as a ordinary mean, or if you need confidence intervals, compute empirical quantiles.</li> </ol>
Tags
Title
singulars
PostAcceptedAnswerId
1. This table or related slice is empty.
PostParentId
1. POun-log a times series while using the package forecast
 singulars
 PostTypePostTypeId
 PTQuestion
PostTypePostTypeId
1. PTAnswer
UserLastEditorUserId
1. USJouni Helske
UserOwnerUserId
1. USJouni Helske
plurals
PostLinksPostIdRelatedPostId
1. This table or related slice is empty.
PostLinksRelatedPostIdPostId
1. This table or related slice is empty.
PostsAcceptedAnswerId
1. This table or related slice is empty.
PostsParentIdCreationDate
1. This table or related slice is empty.
VotesPostIdCreationDate
1. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTUpMod
2. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTUpMod
3. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTUpMod
CommentsPostId
1. This table or related slice is empty.

Querying!

Guidance

A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.