The Altair Community is migrating to a new platform to provide a better experience for you. The RapidMiner Community will merge with the Altair Community at the same time. In preparation for the migration, both communities are on read-only mode from July 15th - July 24th, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here.

ARIMA parameter configuration p, q, d

BarclaeysBarclaeys Member Posts: 18 Learner I
edited August 2020 in Help

I am fairly new to data science and exploring time-series. I'm currently trying the ARIMA model but notice there is a big difference in the outcome of the model by configuring the p, q and d parameters. Is there anyone who can explain in simple words what each parameter means and how I can come up with the best configuration? Or should I use the default and use a parameter optimization?

I hope someone can share his/her experience.

Best Answer


  • Options
    BarclaeysBarclaeys Member Posts: 18 Learner I
    Martin, once more thanks for your feedback. Is my understanding correct that to determine the best setting for the auto-regression, I should run an ACF on my data and check for how many lags I still see a specific correlation? And If so, is there anything similar that I can run for the MA part?
    Thanks, Bart
  • Options
    MartinLiebigMartinLiebig Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,525 RM Data Scientist

    i think there are in general two schools of thought here, when it comes down to hyper parameter settings

    The Statisticians Way: Analyze the data and check what the right parameters of the algorithms should be. For example with ACF, but also other methods. For ARIMA I am not sure if there is a standard test to figure it out. @David_A and @yyhuang are bigger experts on this topic.

    The Data Scientists Way: Just try many p/q/d values and find the best ones by doing a proper out-of-sample test.

    I am a fan of #2, but this does not mean that #1 is wrong.

    - Sr. Director Data Solutions, Altair RapidMiner -
    Dortmund, Germany
Sign In or Register to comment.