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My aim is to compare the forecast performance of several time series models. I have a bivariate dataset, and applied three different models to it:

1) A univariate Arima model (applied to the first variable) using the automatic order selection function 'auto.arima()'. The estimated model is Arima(1,1,1)

2) A Verctorautoregression using both variables. The recognized model (using the package 'vars') is VAR(1), and

3) A univariate state space model (aggain applied to the first variable) using the package 'dlm'. I specified a state space form of Arima(1,1,1) model, as it was suggested by 'auto.arima()', namely I constructed a model consisting of a stochastic trend and of an arma model with one parameter ar and one ma (for details see the code).

I then generated forecasts compared the results graphically and was surprised to see, how poorly my state space model performs. The results of Arima are quite similar, only the VAR(2) model performs relatively well.

Is this poor result of state space model realistic or is my model specification wrong?

data <- read.table(...)
library(vars)
library(forecast)
library("dlm", lib.loc="C:/Users/incognito/Documents/R/win-library/3.0")

# subsetting the data:
data.s<-data[1:528,1]

# Estimation of univariate Arima model and generating a forecast:
arima.m<-auto.arima(data.s.g)
arima.f<-forecast(arima.m,h=30)

# Estimation of a state space representation of Arima(1,1,1) model and forecast:
level0 <- data.s.g[1]
slope0 <- mean(diff(data.s.g))
buildGap <- function(u) {
  trend <- dlmModPoly(dV = 1e-7, dW = exp(u[1 : 2]),
                      m0 = c(level0, slope0),
                      C0 = 2 * diag(2))
  gap <- dlmModARMA(ar = ARtransPars(u[4]),ma=u[5], sigma2 = exp(u[3]))
  return(trend + gap)}

init <- c(-3, -1, -3, .4, .4)
outMLE <- dlmMLE(data.s.g, init, buildGap)
dlmGap <- buildGap(outMLE$par)
filt<-dlmFilter(data.s.g,dlmGap)
forc<-dlmForecast(filt,nAhead=30)

# A bivariate VAR model and forecast:
var<-VAR(data.s)
var.f<-predict(var,n.ahead=30)

# Plotting the results:
plot(data.s.g,xlim=c(400,560),ylim=c(1.5,4),type="l")
lines(529:558,forc$f)
lines(529:558,var.f$fcst$gas[,1],col=3)
lines(529:558,data$gas[529:558],col=4)
lines(529:558,arima.f$mean,col=2)
legend("topleft",legend=c("state space","arima","var"),lty=1,col=c(1,2,3))

enter image description here

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  • $\begingroup$ Try auto.arima(data.s.g, stepwise=FALSE) $\endgroup$ – Zach Aug 2 '13 at 19:27
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First, is your subsetting statement mistyped? It appears you mean something like:

data.s<-data[1:528,]
data.s.g<-data.s[,1]

You might even want to show us a sample of your data (dput), which would let us process it to get an answer more like what you're expecting -- though not using an ARIMA(1,1,1) model.

Second, it looks like you might be training your VAR on the entire data and then predicting the last part, while training your ARIMA and SS on only the first part of the data? (In addition to which, VAR has two time series to work with.)

Third, you're expecting too much of your ARIMA. (If you look into the internals of the Arima object returned by auto.arima, you can find the state space model that R uses under the hood: arima.m$model.) An AR(1) uses only the current data point to make its next prediction, which is not much information.

auto.arima isn't magic. It knows nothing about your data and looks through a limited window of options. If you know more, like perhaps the data has a natural 100-period cycle, you can add that and get much better results.

Fourth, be careful that you've got your dlm model wired together correctly. It seems like there may be one more state than you think there is.

EDIT: Now that you've posted your data, it looks a lot like stock prices, which you're not going to predict with any canned methods.

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  • $\begingroup$ Hello Wayne, to 1.: yes, it was mistyped. to 2.: yes, I am training VAR on the entire data and the univariate ones only on the first variable. I am actually interested in forecastinf the first variable. Do you have any suggestion which additional state might be included in the ss? And a more general question: So far I have not seen any function/technique in the package dlm or in the book of Petris to suggest a model. So, do I have to know in advance which model I would like to have in ss representation before using the package, or is there some technique which I missed? $\endgroup$ – DatamineR Aug 2 '13 at 21:01
  • $\begingroup$ Is it possible to include the data here in my question? $\endgroup$ – DatamineR Aug 2 '13 at 21:03
  • $\begingroup$ So your comparison to the VAR is way optimistic, since it "knows the answers while you're asking ARIMA and SS to predict. As far as I know, SS isn't really different than most other methods: you do need to have some idea about models that are reasonable and motivated. Then start simple and add features. You only graph about 400-550, but it's possible that there is a cyclical movement that you could model. (It peaks near 400, then again near 500, but I'm just guessing here.) $\endgroup$ – Wayne Aug 2 '13 at 21:08
  • $\begingroup$ I'd definitely include the data in your question, if there are no restrictions on your end. Also an explanation of where it comes from helps a lot, since that's a huge part of modeling something. Look at R's dput command to put it into a format that makes it easy for others to use. Other folks may simply graph it themselves or throw something quick at it and give up, but without it we're stuck with generalities. (I'll add that it sounds to me like you have expectations that modeling data is easier or more automatic than it is.) $\endgroup$ – Wayne Aug 2 '13 at 21:10
  • $\begingroup$ My goal is, as I mentioned in my question, to compare different models' forecasting ability. So, considering what you say: "As far as I know, SS isn't really different than most other methods..." it doesnt make much sence to consider respective ss representations of other models which I want to compare, and it is very frustrating, because I spent pretty much time trying to understand the state space world! :D $\endgroup$ – DatamineR Aug 2 '13 at 21:16

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