turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

Find a Community

- Home
- /
- Analytics
- /
- Forecasting
- /
- ARIMA model

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

06-20-2016 06:37 AM

Dear SAS experts,

Appreciate if someone of you hint me on the following questions?

1. What is the difference between proc timeseries and proc ARIMA?

2. What is white noise? Can we've a White Noise in stationary series data (after differencing) and also in the residuals?

3. How to interpret ACF and PACF plot?

4. Although I find several example for ARIMA model, I've trouble finding the document with simpler ARIMA model which has explained step by step apporach with simple terms. Appreciate if you find any easy understanding document for me.

Thanks in advance for any help you offer me.

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

Posted in reply to Babloo

06-20-2016 08:04 AM

Only a part of the answers (any search engine should yield plenty of infos):

1. Use proc timeseries for time series diagnostics (find trend or seasonality). You can do time series diagnostics with proc arima as well, but also use it for forcasting.

2. White Noise means, that there is no "information" (left) in a time series - values have zero mean, a non-zero variance and are serially not correlated (check wikipedia or so for full definition). I don't really understand the 2nd part of your question.

3. Plenty of stuff on the internet/any time series text book (search for ar(1), ma(1), ar(2), ma(2))

4. Maybe like this: 1. Check for unit-roots (check if time series is stationary; search "Dickey-Fuller-Test") 2. Use ACF/PACF for preliminary model 3. Adjust model if necessary (maybe use AIC/SBC as criterion - smaller is better) 4. Make sure the residuals are White Noise