I have a question / confusion about stationary series required for modeling with ARIMA(X). I am thinking of this more in terms of inference (effect of an intervention), but would like to know if forecasting versus inference makes any difference in the response.

Question:

All the introductory resources I have read state that the series needs to be stationary, which makes sense to me and that is where the "I" in arima comes in (differencing).

What confuses me is the use of trends and drifts in ARIMA(X) and implications (if any) for stationary requirements.

Does the use of either a constant/drift term and/or trend variable as an exogenous variable (i.e. adding 't' as a regressor) negate the requirement of the series being stationary? Is the answer different depending on if the series has a unit root (e.g. adf test) or has a deterministic trend but no unit root?

OR

Does a series always have to be stationary, made so via differencing and/or detrending before using ARIMA(X)?

up vote 10 down vote accepted

Looking at the comments it seems that we didn't address the question about how to choose between a deterministic or stochastic trend. That is, how to proceed in practice rather than the consequences or properties of each case.

One way to proceed is the following: Start by applying the ADF test.

  • If the null of a unit root is rejected we are done. The trend (if any) can be represented by a deterministic linear trend.
  • If the null of the ADF test is not rejected then we apply the KPSS test (where the null hypothesis is the opposite, stationarity or stationarity around a linear trend).

    o If the null of the KPSS test is rejected then we conclude that there is a unit root and work with the first differences of the data. Upon the first differences of the series we can test the significance of other regressors or choose an ARMA model.

    o If the null of the KPSS test is not rejected then we would have to say that the data are not much informative because we weren't able to reject none the of the null hypotheses. In this case it may be safer to work with the first differences of the series.

As mentioned in a previous answer, remember that these tests may be affected by the presence of outliers (e.g. an outlier at a single time point due to an error when recording the data or a level shift due for example to a policy change that affects the series from a given time point on). Thus, it is advisable to check these issues as well and repeat the previous analysis after including regressors for some potential outliers.

  • Awesome! Was my comment question above correct that if we see what looks like a trend, we use the ADF test that includes a trend (#3 option in the link I posted)? – B_Miner Jul 12 '14 at 11:51
  • Last question - what do you make of the situation where you fit an ARIMA, say ARIMA(0,1,1) to a series and the mean of the difference is non-zero? This means I believe that you add a constant to the model - which also represents a linear trend in the original series. What does this case signify? Is the trend in the original series deterministic because differencing the series did not remove the trend? – B_Miner Jul 12 '14 at 11:54
  • @B_Miner Regarding your first comment, I would start by including only an intercept. If the trend looks somewhat exponential you can also add the slope parameter of the linear trend and see if it is significant. In general, it is better to start with a model with few parameters and if the diagnostic of the residuals is not satisfactory then consider adding other elements. – javlacalle Jul 12 '14 at 15:33
  • @B_Miner Taking first differences removes both a deterministic and a stochastic trend. If you see a trend in the differenced series in a model with intercept, then you should consider taking differences again (i.e. test for a second unit root). – javlacalle Jul 12 '14 at 15:33
  • 1
    @pidosaurus lack of agreement between and ADF and KPSS tests may be due to small sample size, presence of outliers, non-linear trend,... if after further inspection still not clear which one is more appropriate, it may be safer to consider the presence of a unit root. A quick view to your data suggested to me the presence of a non-linear trend, a quadratic trend of the form $a_1 t + a_2 t^2$ may be appropriate. – javlacalle May 22 '17 at 6:47

Remember that there are different kinds of non-stationarity and different ways on how to deal with them. Four common ones are:

1) Deterministic trends or trend stationarity. If your series is of this kind de-trend it or include a time trend in the regression/model. You might want to check out the Frisch–Waugh–Lovell theorem on this one.

2) Level shifts and structural breaks. If this is the case you should include a dummy variable for each break or if your sample is long enough model each regimé separately.

3) Changing variance. Either model the samples separately or model the changing variance using the ARCH or GARCH modelling class.

4) If your series contain a unit root. In general you should then check for cointegrating relationships between the variables but since you are concerned with univariate forecasting you shoud difference it once or twice depending on the order of integration.

In order to model a time series using the ARIMA modelling class the following steps should be appropriate:

1) Look at the ACF and PACF together with a time series plot to see wheter or not the series is stationary or non-stationary.

2) Test the series for a unit root. This can be done with a wide range of tests, some of the most common being the ADF test, the Phillips-Perron (PP) test, the KPSS test which has the null of stationarity or the DF-GLS test which is the most efficient of the aforementioned tests. NOTE! That in case your series contain a structural break these tests are biased towards not rejecting the null of a unit root. In case you want to test the robustness of these tests and if you suspect one or more structural breaks you should use endogenous structural break tests. Two common ones are the Zivot-Andrews test which allows for one endogenous structural break and the Clemente-Montañés-Reyes which allows for two structural breaks. The latter allows for two different models. An additive outlier model which accounts for sudden changes in the slope of the series and an innovative outlier model which takes gradual changes into account and allows a break in the intercept and slope.

3) If there is a unit root in the series then you should difference the series. Afterwards you should run look at the ACF, PACF and the time series plot and probably check for a second unit root to be on the safe side. The ACF and PACF will help you decide on how many AR and MA terms you should be including.

4) If the series does not contain a unit root but the time series plot and the ACF show that the series has a deterministic trend you should add a trend when fitting the model. Some people argue that it is completely valid to just difference the series when it contains a deterministic trend although information may be lost in the process. Never the less its a good idea to difference it in order to see have many AR and/or MA terms you will need to include. But a time trend is valid.

5) Fit the different models and do the usual diagnostic checking, you might want to use an information criterion or the MSE in order to select the best model given the sample you fit it on.

6) Do in sample forecasting on the best fitted models and calculate loss functions such as MSE, MAPE, MAD to see which of them actually perform best when using them to forecast because that is what we want to do!

7) Do your out of sample forecasting like a boss and be pleased with your results!

  • And to answer your question quickly. Yes it can. – Plissken Jul 11 '14 at 21:57
  • I wasn't allowed to comment on the above since I dont have enough reputation but I want to point out that a white noise process is stationary. Its mean and variance does not change over time so it is stationary! – Plissken Jul 11 '14 at 22:00
  • Dan, great answer! Regarding your last comment here, are you saying that if you end up with white noise residual, which is stationary....then it tells you that you have modeled the series adequately (i.e. dealt with issues of stationarity properly / adequately)? – B_Miner Jul 12 '14 at 1:27
  • 1
    Yes exactly. Often the Ljung-Box Q test is used to test the residuals in order to see whether or not they are random. In case they are then the model is an adequate representation of the data. Something which I forgot to mention in the above answer is also that after you have fitted the model you can look at the ACF and time series plot of the residuals of the fitted series. This will give you a good indication whether or not your residuals are white noise or not (the ACF should not have any significant lags). Anyways, most statistical packages have a command for the Ljung-Box Q test. – Plissken Jul 12 '14 at 1:40
  • Here is the wiki link for the Ljung-Box Q test: en.wikipedia.org/wiki/Ljung%E2%80%93Box_test – Plissken Jul 12 '14 at 1:42

Very interesting question, I would also like to know what others have to say. I'm an engineer by training and not a statistician, so someone can check my logic. As engineers we would like to simulate and experiment, so I was motivated to simulate and test your question.

As empirically shown below, using a trend variable in ARIMAX negated the need for differencing and makes the series trend stationary. Here is the logic I used to verify.

  1. Simulated an AR process
  2. Added a deterministic trend
  3. Using ARIMAX modeled with trend as exogenous variable the above series without differencing.
  4. Checked the residuals for white noise and it is purely random

Below is the R code and plots:

set.seed(3215)

##Simulate an AR process
x <- arima.sim(n = 63,list(ar = c(0.7)));
plot(x)

## Add Deterministic Trend to AR
t <- seq(1, 63)
beta <- 0.8
t_beta <- ts(t*beta,frequency=1)
ar_det <- x+t_beta
plot(ar_det)

## Check with arima

ar_model <- arima(ar_det,order=c(1,0,0),xreg=t,include.mean=FALSE)

## Check whether residuals of fitted model is random

pacf(ar_model$residuals)

AR(1) Simulated Plot enter image description here

AR(1) with deterministic trend enter image description here

ARIMAX Residual PACF with trend as exogenous. Residulas are random,with no pattern left enter image description here

As can be seen above, modeling deterministic trend as an exogenous variable in the ARIMAX model negates the need for differencing. Atleast in the deterministic case it worked. I wonder how this would behave with stochastic trend which is very hard to predict or model.

To answer your second question, YES all ARIMA including ARIMAX have to be made stationary. At least that's what text books say.

In addition, as commented, see this article. Very clear explanation on Deterministic Trend vs. Stochastic trend and how to remove them to make it trend stationary and also very nice literature survey on this topic. They use it in the neural network context, but it is useful for general time series problem. Their final recommendation is when it is clearly identified as deterministic trend, the do linear detrending, else apply differencing to make the time series stationary. The jury is still out there, but most researchers cited in this article recommend differencing as opposed to linear detrending.

Edit:

Below is random walk with drift stochastic process, using exogenous variable and difference arima. Both appear to give same answer and in essence they are same.

library(Hmisc)

set.seed(3215)

## ADD Stochastic Trend to simulated Arima this is AR(1) with unit root with non zero mean

y = rep(NA,63)
y[[1]] <- 2


for (i in 2:63)  {
y[i] <-3+1*y[i-1]+ rnorm(1, mean = 0, sd = 1)
} 

plot(y,type="l")

y_ts <- ts(y,frequency=1)

## Lag to create Xreg

y_1 <- Lag(y,shift=1)


## Start from 2 value to avoid NA and make it equal length with xreg

y <- window(y_ts,start =2,end=63)
xreg1 <- y_1[-1]

## Check the values with ARIMA and xreg

g <- arima(y,order=c(0,0,0),xreg=xreg1)

pacf(g$residuals)

## Check the values with ARIM

g1 <- arima(y,order=c(0,1,0))

pacf(g1$residuals)

## 

ARIMA(0,0,0) with non-zero mean 

Coefficients:
      intercept   xreg1
         3.1304  0.9976
s.e.     0.2664  0.0025

Hope this helps!

  • I am interested in others views too - I am not sure, does the residual being white noise meet the requirement for the series being stationary - i.e. if you achieve white noise can you be satisfied? Or, is the inclusion of the exogenous variable actually acting to "de-trend" and make this series stationary? I wonder if a check on this later question is if you get the same model (ar1 coefficient, etc) if you de-trend with linear regression and then fit the arima(1,0,0)...I did this and the results are close. So maybe adding the exogenous variable is the same as detrending. – B_Miner Jul 11 '14 at 12:28
  • Yes it is the same, few months back I came across this in a neural network forecasting article. I'll provide reference if I find it. – forecaster Jul 11 '14 at 12:56
  • Any idea about the case when there is a unit root or when there is a constant in the model? – B_Miner Jul 11 '14 at 13:22
  • I have updated my answer with the article I was referencing. – forecaster Jul 11 '14 at 17:38
  • I think you may have not saved it. – B_Miner Jul 11 '14 at 18:24

Determining whether the trend (or other component such as seasonality) is deterministic or stochastic is part of the puzzle in time series analysis. I will add a couple of points to what has been said.

1) The distinction between deterministic and stochastic trendsis important because if a unit root is present in the data (e.g. a random walk) then test statistics used for inference do not follow the traditional distribution. See this post for some details and references.

We can simulate a random walk (stochastic trend where first differences should be taken), test for the significance of deterministic trend and see the percentage of cases in which the null of deterministic trend is rejected. In R, we can do:

require(lmtest)
iter <- 10000
cval <- 0.05
n <- 120
rejections <- 0
set.seed(123)
for (i in seq.int(iter))
{
  x <- cumsum(rnorm(n)) # random walk
  fit <- lm(x ~ seq(n))
  if (coeftest(fit)[2,"Pr(>|t|)"] < cval)
    rejections <- rejections + 1
}
100 * rejections / iter
#[1] 88.67

At the 5% significance level, we would expect to reject the null in the 95% of cases, however, in this experiment it was rejected only in ~89% of cases out of 10,000 simulated random walks.

We can apply unit root tests to test whether a unit root is present. But we must be aware that a linear trend may in turn lead to failure to reject the null of a unit root. In order to deal with this, the KPSS test considers the null of stationarity around a linear trend.

2) Another issue is the interpretation of the deterministic components in a process in levels or first differences. The effect of an intercept is not the same in a model with a linear trend as in a random walk. See this post for illustration.

Analytically, let's take a random walk with drift: $$ y_t = \mu + y_{t-1} + \epsilon_t \,,\quad \epsilon_t \sim NID(0, \sigma^2) \,. $$

If we substitute repeatedly $y_{t-i}$ by lagged versions of $y_t$:

\begin{eqnarray*} y_t &=& \mu + \underbrace{y_{t-1}}_{\mu + y_{t-2} + \epsilon_{t-1}} + \epsilon_t \\ &=& 2\mu + \underbrace{y_{t-2}}_{\mu + y_{t-3} + \epsilon_{t-2}} + \epsilon_{t-1} + \epsilon_t \\ &=& 3\mu + y_{t-3} + \epsilon_{t-2} + \epsilon_{t-1} + \epsilon_t \\ &...& \end{eqnarray*}

We arrive to:

$$ y_t = y_0 + \mu t + \sum_{i=1}^t \epsilon_i $$

where $y_0$ is some arbitrary initial value. Thus, we see that the accumulation of shocks and the long memory of the random walk makes the intercept $\mu$ to have the effect of a linear trend with slope $\mu$ (in this case the constant term $\mu$ is called a drift).

If the graphical representation of a series shows a relatively clear linear trend, we cannot be sure whether it is due to the presence of a deterministic linear trend or to a drift in a random walk process. Complementary graphics and tests statistics should be applied.

There are some caveats to bear in mind since an analysis based on unit root and other test statistics is not foolproof. Some of these tests may be affected by the presence of outlying observations or level shifts and require the selection of a lag order which is not always straightforward.

As a workaround to this puzzle, I think that the common practice is to take differences of the data until the series looks stationary (for example looking at the autocorrelation function, which should go to zero fast) and then choose an ARMA model.

  • Greta post- you are clearly a great asset to this site! I was curious looking here and your other posts - is it possible to use a ADF or KPSS test to determine if what looks like a trend in the series in levels is deterministic or stochastic? I found this : faculty.smu.edu/tfomby/eco6375/BJ%20Notes/ADF%20Notes.pdf which makes it look like if you see a trend visually in the series, use option #3 of the test and if you dont reject the null, you have evidence for a deterministic trend. – B_Miner Jul 12 '14 at 1:56
  • Based on Dan's comment, I guess if you get white noise residuals, none of this matters :) – B_Miner Jul 12 '14 at 2:00

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