I know from nothing about arimax or the forecast package, but it sounds
to me like you are expecting magic. An analogy:
Suppose you are doing a simple linear regression of y on x1 and x2 with
y and x1 having 49 entries each, but with x2 having only 40. Assume
that y, x1 and x2 are properly aligned but there are no x2 data for the
last 9 entries of x2. Would you really expect to be able to make use of
the last 9 entries of y and x1 in fitting your model?
I could be wrong (I was once; back in 1968 --- I thought I'd made a
mistake and I hadn't; :-) ) but I don't think there is any scope for
applying an EM algorithm, since x2 is not a random variable so taking
expectations of the likelihood w.r.t. the missing data makes no sense.
I guess if you assumed (e.g.) a joint Gaussian distribution for
(y,x1,x2) then you could "do EM". But that would not be a realistic
assumption in most instances.
cheers,
Rolf Turner
--
Technical Editor ANZJS
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276
On 14/01/16 10:12, Lorenzo Isella wrote:
Dear All,
Please consider the small self-contained code at the end of the email.
It is an artificial arimax model with a matrix of regressors xreg.
In the script, xreg has as many rows as the number of data points in
the time series "visits" I want to model.
Now, my problem is the following: I am in a similar situation, as in
the example, but my matrix of auxiliary regressors is "shorter" than
the time series, e.g. I am trying to do something like (see the last
line of my script)
modArima <- auto.arima(visits, xreg=xreg[1:40, ])
Which is simply not allowed by the forecast package.
Is there any workaround?
I do not want to throw away the information, so I would prefer *not*
to disregard the predictors just because they are not synchronized to
the time series, nor shorten artificially the time series because it
is longer than my predictor matrix.
Any suggestion is appreciated.
Regards
Lorenzo
########################################################################
library(forecast)
# create some artifical data
modelfitsample <-
data.frame(Customer_Visit=rpois(49,3000),Weekday=rep(1:7,7),
Christmas=c(rep(0,40),1,rep(0,8)),Day=1:49)
# Create matrix of numeric predictors
xreg <-
cbind(Weekday=model.matrix(~as.factor(modelfitsample$Weekday)),
Day=modelfitsample$Day,
Christmas=modelfitsample$Christmas)
# Remove intercept
xreg <- xreg[,-1]
# Rename columns
colnames(xreg) <-
c("Mon","Tue","Wed","Thu","Fri","Sat","Day","Christmas")
# Variable to be modelled
visits <- ts(modelfitsample$Customer_Visit, frequency=7)
# Find ARIMAX model
modArima <- auto.arima(visits, xreg=xreg)
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