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Archive for October, 2008

Time Series (1)

Posted by phillippeng on October 15, 2008

Text and Resources on time series:

1. Enders, Walter (2004). Applied Econometrics Time Series, 2nd edition, New York: Wiley & Sons, Inc.
2. Wei, William W.S. (1990). Time Series Analysis: Univariate and Multivariate Methods, New York: Addison-Wesley Publishing Co., Inc.
3. Box, G.E.P., G.M. Jenkins and G.C. Reinsel (1994). Time Series Analysis Forecasting and Control, Third Edition, San Francisco: Holden-Day, Inc.
4. Lutkepohl, Helmut (2005). New Introduction to Multiple Time Series Analysis, 3rd Edition, Springer Verlag.

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Scoring observations using PROC FASTCLUS

Posted by phillippeng on October 6, 2008

PROC FASTCLUS can be used to perform a k-means clustering for observations. All the observations in the training dataset are assigned to clusters on the basis of the parameterization of the procedure and of their variable values. Scoring the observations in the validation dataset using PROC FASTCLUS seems a little bit challenging because the cluster assignment rules depend on new observations now.

Scoring new observations without changing the cluster assignment rules can be achieved by using a SEED dataset in PROC FASTCLUS.

/*original clustering */

%let indsn = input;  *your input dataset;
%let nclus = maxclus; *number of clusters to request;
%let indvars = varlist; *independent variables to run proc fastclus on;
%let valid = val_data; *validation dataset to score;

proc fastclus data=&indsn maxclusters = &nclus outseed= clusterSeeds;
var &indvars;

/*scoring new observations using the seed dataset */
proc fastclus data=&valid  out=&valid._scored seed = clusterSeeds maxclusters = &nclus maxiter = 0;
var &indvars;

“Data Preparation for Analytics Using SAS” By Gerhard Svolba, Gerhard Svolba, Ph.D.

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Time Management

Posted by phillippeng on October 3, 2008

This afternoon I attended the internal training session “Time Management Essentials” instructed by Susan Connors. Susan provided lots of tips and principles of managing times. It’s an informative session. Everybody needs to learn how to manage their time so that they can be more productive.

Time management is not about how much we know about time management, but about how much we put into action. In another word, it’s not about science but behaviour.

Here are some tips/views shared:

1. Manage your email at once. Don’t check your email every 3 to 5 minutes. However, we need to balance the client expectation and email management efficiency.

2. Retool the priority system. If we have the following labels:
Urgent and goal-related = A
Goal-related but not urgent = B
Urgent but not goal-related = C
we need to have have at least one “B” priority in our to-do list every day.

3. Use the primary energy time for priority tasks.

You can find more free resources through


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PROC GAM – Detect the Seasonality

Posted by phillippeng on October 2, 2008

Fluctuations embedded in a data series over time may come from random fluctuations or latent seasonality. Very often, we need to test if there’s a true seasonality trend. If so, what seasonality pattern is it? The example in the following link provides a perfect illustration on how to examine the seasonality using SAS PROC GAM.

proc gam

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