
07-08-2024
udo_sas
SAS Employee
Member since
06-23-2011
- 417 Posts
- 27 Likes Given
- 43 Solutions
- 196 Likes Received
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Latest posts by udo_sas
Subject Views Posted 1538 06-14-2017 12:53 PM 968 05-25-2017 02:59 PM 1264 05-24-2017 08:00 AM 2314 05-19-2017 07:51 AM 2361 05-12-2017 08:45 AM 1409 04-12-2017 08:20 AM 1760 04-05-2017 09:18 AM 10015 03-22-2017 05:23 PM 2514 02-28-2017 01:55 PM 1128 02-20-2017 09:18 AM -
Activity Feed for udo_sas
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- Posted Re: How does Box Cox parameters work? on SAS Forecasting and Econometrics. 06-14-2017 12:53 PM
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- Posted Re: Time Series Forecasting: Holt Winter Model(Additive) on SAS Forecasting and Econometrics. 02-20-2017 09:18 AM
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Posts I Liked
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Subject Likes Posted 1 02-03-2012 04:03 PM 1 12-02-2016 01:58 PM 1 11-28-2016 06:21 PM 1 11-21-2016 08:24 AM 1 09-09-2016 04:49 PM
06-14-2017
12:53 PM
Hello -
You will find some details here:
http://support.sas.com/documentation/cdl/en/etsug/66840/HTML/default/viewer.htm#etsug_tffordet_sect008.htm
Thanks,
Udo
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05-25-2017
02:59 PM
Hello -
This may happen if you are using a hierarchy and reconciliation.
In the modeling view you see information before your hierarchy is reconciled. Basically it shows you which models were considered and which model was chosen.
In the forecasting view you will see the impact of running reconciliation - which basically makes sure that the numbers add up across a hierarchy.
You can overlay the forecasting model plot by selecting "View -> Edit Forecasting View Properties" and check "Selected model":
This will allow you to visualize the impact of reconciliation.
Thanks,
Udo
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05-24-2017
08:00 AM
Hello -
For a free online resource you may want to consult: https://www.otexts.org/fpp/
Thanks,
Udo
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05-19-2017
07:51 AM
Hello -
You may have set up authorizations for your libraries incorrect.
If you consult Chapter 6 of the Administrator’s Guide: "Creating and Configuring Libraries Understanding" and check out "Libraries Assigned by SAS Forecast Server" you will find details on how to go about this task.
If still in doubt my colleagues in Technical Support will be delighted to assist.
Thanks,
Udo
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05-12-2017
08:45 AM
Hello -
Here are four main sources of external library assignments:
All SAS libraries that are shipped with SAS are automatically assigned to each SAS session. Examples of Base SAS libraries are Sashelp, Sasuser, and Work.
Additional libraries can be assigned by adding LIBNAME statements to your SAS or server configuration files. The scope of the configuration file (in other words, is it a SAS or server configuration file) determines the availability of the library.
Libraries can also be assigned by including LIBNAME statements in the start-up code for an environment. These libraries are available only for the associated environment. If you assign a library in your environment’s start-up code, you must include a corresponding LIBNAME CLEAR statement in the shutdown code.
Pre-assigned libraries that are defined in the metadata and associated with a given server are automatically assigned when the server session is created.
More details can be found in the SAS® Forecast Server 14.2: Administrator’s Guide - Chapter 6: Creating and Configuring Libraries
Thanks,
Udo
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04-12-2017
08:20 AM
Hello -
You may want to look into using "adjustment variables" - from SAS Forecast Studio 14.2 User's Guide (chapter 6):
"What Is an Adjustment Variable?
Systematic variations and deterministic components are included in time series data. By adjusting the time series for known systematic variations or deterministic components, the underlying stochastic (unknown) time series process can be more readily identified and modeled. For more information about adjustment variables, see the "Adjustment Step" topic in the SAS Forecast Server Procedures: User’s Guide."
Thanks,
Udo
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04-05-2017
09:18 AM
Hello - This book provides a good overview on common approaches: https://g.co/kgs/2243UZ However, in my mind the approach outlines in this white paper: https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/combining-analytics-structured-judgment-107393.pdf works well, keeping in mind that forecasting new products is most challenging. Thanks, Udo
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03-22-2017
05:23 PM
Hello -
If you have access to SAS/ETS software you may want to use the TIMESERIES procedure - in particular the OUTSUM data set should be of interest.
Example:
proc timeseries data=sashelp.citiday outsum=work.outsum out=_null_; id date interval=day accumulate=total; var SNYDJCM; run;
Thanks,
Udo
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02-28-2017
01:55 PM
Hello -
If I understand your question correctly you are mostly wondering about the effect of "lead 10", correct?
proc expand data=random out=want; id date; convert x = x_movSumWRONG /transformout = (lead 10 reverse movesum 10 reverse); /*suggest via Tom*/ run;
Your data (random) looks like this - leaving out some data in the middle:
date
x
1-Jan-16
114.68
2-Jan-16
83.68
3-Jan-16
111.5
4-Jan-16
86.82
5-Jan-16
103.46
6-Jan-16
92.85
7-Jan-16
88.22
8-Jan-16
98.48
9-Jan-16
99.5
10-Jan-16
132.27
11-Jan-16
87.05
12-Jan-16
99.98
13-Jan-16
75.69
…
…
20-Dec-16
93.67
21-Dec-16
114.5
22-Dec-16
94.1
23-Dec-16
96.01
24-Dec-16
104.5
25-Dec-16
104.69
26-Dec-16
116.58
27-Dec-16
98.41
28-Dec-16
103.41
29-Dec-16
110.42
30-Dec-16
90.44
31-Dec-16
88.62
Lead 10 will tell EXPAND to forget about the first 10 observations (obs) - so your time series now starts at 87.05 - you will introduce missing values are the end of your series (unless you would add SETMISS 0, which would append 0s instead).
Reverse will tell EXPAND to reverse the order, the missing values you introduced will be your new first obs, 87.05 your new last obs.
Movesum 10 will create the backward moving sum of 10 obs.
Finally Reserve will put the series into its original order again.
If you decompose your "wrong" statement into:
proc expand data=random out=want; id date; convert x = x_lead10 /transformout = (lead 10); convert x = x_reverse /transformout = (lead 10 reverse); convert x = x_movsum10 /transformout = (lead 10 reverse movsum 10); convert x = x_reverseII /transformout = (lead 10 reverse movsum 10 reverse); run;
you will see these columns in WANT.
In my mind this is the expected behaviour - it is just not the same what you'd like to archive with:
convert x= x_movSum /transformout = (movsum 10 trimleft 9);
Hope this makes sense,
Udo
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02-20-2017
09:18 AM
Hello -
You may want to have a look at: http://go.documentation.sas.com/?docsetId=etsug&docsetVersion=14.2&docsetTarget=etsug_tffordet_sect016.htm&locale=en#etsug_tffordet000750
Details are provided here: http://go.documentation.sas.com/?docsetId=etsug&docsetVersion=14.2&docsetTarget=etsug_tffordet_sect021.htm&locale=en#etsug_tffordet001867
Thanks,
Udo
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01-26-2017
10:21 AM
Hello -
Not sure if this paper provides you with what you are looking for: http://analytics.ncsu.edu/sesug/2010/SDA12.Dickey.pdf, but it might point you in the correct direction.
Thanks,
Udo
... View more
12-05-2016
05:30 PM
Hello -
It may be worthwhile to run PROC ESM instead on PROC FORECAST.
Here is an example which should work for your data (even when adding several stores):
proc esm data=production plot=forecasts lead=&lead outfor=work.AFORECASTOUTPUT;
forecast sales/model=damptrend;
by store;
run;
Of course ESM will assume the same model for all stores (in my code I used a damped trend exponential smoothing model, which seems to be appropriate for your example), but at least the parameters for each store will be different.
Thanks,
Udo
... View more
12-02-2016
01:58 PM
1 Like
Hello -
Please excuse for delay in responding.
1. What is the logic with BACK option?
Please check out: https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/out-of-sample-range-and-holdout-sample/m-p/225886/highlight/true#M1387 which should give you some hints.
2. Currently ESM does not allow you to incorporate inputs like logprice. My suggestion would be to look at UCM instead. Check out: http://support.sas.com/documentation/cdl/en/etsug/68148/HTML/default/viewer.htm#etsug_ucm_examples04.htm to get started.
3. You will find some additional books here:
https://www.sas.com/store/books/products-solutions/sas-ets/cBooks-cbooks_productsandsolutions-cbooks_productsandsolutions_ETS-p1.html
I would also recommend to check out: https://www.otexts.org/fpp
Thanks,
Udo
... View more
11-28-2016
06:21 PM
1 Like
Hello -
Not sure if my response will be useful, but you may want to double check your PROC REG code.
None of the variables used in your "MODEL" statement: model return_f=like1 omx /; seem to be part of your sample data set as far as I can tell. This is probably the reason why your PROC REG code is failing.
You wrote: "Where/how is the out sheet made and what is needed for making this forecast?"
On a very high level the flow should be as such:
a) in step 1 you create a table called "a"
b) in step 2 you will want to use this table in PROC REG - make sure that your model statement only contains variables "a" features
c) in step 2 your OUTPUT statement of PROC REG creates a table called WORK.Reg_stats
d) in step 3 you are using table WORK.Reg_stats in PROC SGPLOT to create a plot
If you get this code to run, you may want to think about if a linear regression model is indeed the best model to run for time series data.
Here is a simple forecast using ESM, which you may want to use as a reference. Instead of importing your Excel sheet I'm replicating your data in a data step and then use PROC ESM to create a forecast for the next 7 days. Note that I'm using the BACK option to compare these forecasts to data which was not used for modeling.
Hope this gives you a jump start.
Thanks,
Udo
data have ;
FORMAT Close BEST12.
Date DATE9.
;
INFORMAT Close BEST11.
Date ANYDTDTE9.
;
INPUT Close
Date
;
cards;
107.955733 8/22/2016
108.293993 8/23/2016
107.478182 8/24/2016
107.020532 8/25/2016
106.393753 8/26/2016
106.274363 8/29/2016
105.458552 8/30/2016
105.558040 8/31/2016
106.184827 9/1/2016
107.179719 9/2/2016
107.149865 9/6/2016
107.806498 9/7/2016
104.981001 9/8/2016
102.603209 9/9/2016
104.901415 9/12/2016
107.398588 9/13/2016
111.199076 9/14/2016
114.979668 9/15/2016
114.332987 9/16/2016
112.999835 9/19/2016
112.989884 9/20/2016
112.969990 9/21/2016
114.034524 9/22/2016
112.134277 9/23/2016
112.303406 9/26/2016
112.512333 9/27/2016
113.367940 9/28/2016
111.606985 9/29/2016
112.472544 9/30/2016
111.945245 10/3/2016
112.422796 10/4/2016
112.472544 10/5/2016
113.308249 10/6/2016
113.477379 10/7/2016
115.457220 10/10/2016
115.705943 10/11/2016
116.740624 10/12/2016
116.382470 10/13/2016
117.029143 10/14/2016
116.949558 10/17/2016
116.869965 10/18/2016
116.521754 10/19/2016
116.462055 10/20/2016
116.004406 10/21/2016
117.049045 10/24/2016
117.645979 10/25/2016
114.999563 10/26/2016
113.895240 10/27/2016
113.139120 10/28/2016
112.960039 10/31/2016
110.920507 11/1/2016
111.019995 11/2/2016
109.830002 11/3/2016
108.839996 11/4/2016
110.410004 11/7/2016
111.059998 11/8/2016
110.879997 11/9/2016
107.790001 11/10/2016
108.430000 11/11/2016
105.709999 11/14/2016
;
run;
proc esm data=have plot=forecasts back=7 lead=7;
id date interval=weekday accumulate=total;
forecast close / model=damptrend;
run;
... View more
11-21-2016
08:24 AM
1 Like
Hello -
One reason might be that using R Square for forecasting models is not a good idea in the first place.
See for example: http://www.forecastingprinciples.com/index.php/2013-01-30-11-08-58/10-practitioner/238-rules-for-cheaters-how-to-get-a-high-r2
Thanks,
Udo
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