I have modeled a stationary time series with another related stationary time series. I'm having a problem with cyclicality in the residuals and I don't know how to fix it.

Here is my model:

$\text{TS}_1(t) = \beta_0 + \beta_1\cdot \text{TS}_2(t) + \epsilon$

Here is a chart of the residuals:


There is clearly a strong trend. I tried to take out the trend by adding a 15 day lagged variable. The new residuals are looking a lot better, but there still looks like there is some kind of trend or abnormality (they don't look random to me).

Here is the model with the Lag:

$\text{TS}_1(t) = \beta_0 + \beta_1\cdot \text{TS}_2(t) + \beta_2\cdot(\text{TS}_2(t-15)) + \epsilon$


I haven't ever done anything like this before. I know adding a lagged variable in AR models can remove seasonality, but I don't know if that applies to the errors on a time series regressed on a different time series.

Is adding a lagged variable to the model the appropriate way of removing trends in the residuals? What tests can I run (other than just looking at the chart) to decide of the trend is still an issue? I ran the Durbin-Watson test (both models failed), but I don't know if the test applies when modeling one time series from another.

  • 1
    $\begingroup$ If you post your data I will be able to advise you as to how to deal with your problem which is probably an omitted ARIMA term required to deal with auto-correlation in the residuals. $\endgroup$
    – IrishStat
    Commented Jan 9, 2017 at 20:34
  • $\begingroup$ @IrishStat Thank you for your willingness to help! Unfortunately for me the data I am working with has privacy restrictions that prevent me from being able to share the actual data points. I will look into the omitted ARIMA terms and try to see if I can find a solution. Thank you again for you help. $\endgroup$
    – Jarom
    Commented Jan 9, 2017 at 21:11
  • $\begingroup$ It could also be an artifact of not having the correct lag structure in X or it could be an artifact of a change in model parameters or error variance over time or even the need to transform Y in some manner as suggested by the Box-Cox test OR even a reflection of untreated outliers/level step shifts . You are observing a symptom and analysis can suggest a possible remedy/cause. $\endgroup$
    – IrishStat
    Commented Jan 9, 2017 at 21:35
  • $\begingroup$ @IrishStat Thanks for the direction. I'll have to do some research and some testing of the data. I'll post if I find a solution. $\endgroup$
    – Jarom
    Commented Jan 9, 2017 at 23:24
  • 1
    $\begingroup$ Sorry for the ambiguity. TS1 and TS2 are two different related time series. $\endgroup$
    – Jarom
    Commented Jan 10, 2017 at 18:37

1 Answer 1


Try adding a moving average MA(q) term, it looks like a spike upwards in your errors is followed by a sharp drop in your errors. An MA(1) would add the term $\theta$ε$_{t-1}$ which would factor in the error from the day before. This might smooth out large movements in your trend.

AR(1),MA(q): TS$_{t}$ = β$_{0}$ + β$_{1}$*TS$_{t-1}$ + β$_{2}$*TS$_{t-15}$ + $\theta_{1}$ε$_{t-1}$+ ...+ $\theta_{q}$ε$_{t-q}$ + ε

  • $\begingroup$ I added a seven day moving average (which I think is logical given the data). It smoothed out my residuals a lot and increased the R-Squared. I'm going to have to look up the technical aspect of adding a previous error term to the regression in SAS. Is the DW test appropriate to quantify if my errors are random enough? $\endgroup$
    – Jarom
    Commented Jan 10, 2017 at 15:38
  • 1
    $\begingroup$ Yep, Durbin Watson tests the autocorrelation of your residuals, since you are not fitting a regression with Arima errors, rather an ARMAX model, you should check for that. By the way, differencing could be an idea since it seems like you have some trend there $\endgroup$ Commented Jan 10, 2017 at 16:17
  • $\begingroup$ @TommasoGuerrini Great advice! I didn't think that I needed to do differencing because my data looked stationary based on the Dickey-Fuller test. But maybe the DF does not catch seasonal non-stationarity? I put a week and month seasonal difference and it has taken care of the auto correlation issue. My quesion now is, does it make sense to use this model? I apalogize that I don't know the proper notation: TS1Diff(Month) = β0 + β 1 ⋅TS 2 Diff(Month) + β 2 ⋅TS 2 Diff(week) $\endgroup$
    – Jarom
    Commented Jan 10, 2017 at 17:37
  • $\begingroup$ @Jarom you should use ACF and PACF graphs to explore your data set to determine whether it is stationary as well as statistical tests. Here is a great tutorial on ARIMA modelling and contains R code if your an R user. otexts.org/fpp/8/1 $\endgroup$ Commented Jan 10, 2017 at 17:47
  • $\begingroup$ @TommasoGuerrini Like I said, I did the seasonal differencing. Now I have stationary data. I created a valid model on that data. Now my question is, is there a technique to take the predicted values from the differenced data and apply it to the original data? $\endgroup$
    – Jarom
    Commented Jan 11, 2017 at 15:50

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