Introduction to SAS Programming

Introductory training session on SAS programming using onDemand. For more information/enrollment for SAS training, check out our course content here, or drop an email to / fill in the form here.

Time Series using Holt’s Linear Exponential Smoothing (Seasonal Variation)

In this video , we explain how to implement Exponential Smoothing on Excel itself to generate a forecast.

We begin by explaining the decomposition of time series into 4 components

  • Trend (Long Term Progression of the Series)
  • Seasonality
  • Cyclic
  • Irregular/Noise

We then demonstrate the use of Moving averages and single exponential smoothing to extract the trend from the series. By subtracting trend from the original signal we can extract the seasonal variation around the trend.

Further we demonstrate the Holt’s technique for double exponential smoothing in a linear upwards trend and how we can use it for forecasting. Furthermore, by using the length of the season, we average out the seasonal fluctuation around the trend (thereby try to eliminate the irregular component) and then combine the forecasted trend and seasonal fluctuation to get an integrated forecast.

All of the above has been demonstrated using MS Excel and simple formulae, and then we proceed to demonstrate the use of IBM SPSS to do the same.

The worksheet with the implementation can be downloaded from here.

Online Batch on SAS Programming (Base and Advanced)



Learn-Analytics is starting an online batch on SAS Programming (Base and Advanced) on Saturday, Jan 14th. Classes are scheduled at 2000 IST (1430 GMT, 0930 Eastern), 3 hours a day. For those wishing to register for the training, the first two classes (6 hours of training) will be free to attend and enabling participants to evaluate the trainer as well as the delivery mechanism.

Medium of training will be through Webex, the instructor will take the participants through hands on sessions using datasets and case studies with exercises at the end of each session. Recordings of the session will be made available to all participants post the training for a period of 3 months.

For the detailed modules design and topics covered, click here. Interested candidates can drop us an email at or fill in the contact form here,  we will forward the webex invitation link for the free evaluation.


Demo on Time Series using Exponential Smoothing (IBM SPSS and Excel)

Following up on our last week’s Webex session on Logistic Regression for credit scoring (you can catch it here), this Sunday we will demonstrate the technique of exponential smoothing in time series forecasting.

More specifically, during the webinar we will take you through the basic decomposition of a time series into its components:

  • Trend
  • Seasonality
  • Cyclic
  • Error

We will concentrate on extracting and forecasting the trend and seasonality of a series. Trend component can be extracted using Exponential smoothing (Single, double and triple depending on the slope and pattern) and building a seasonal index to forecast the seasonal variation.

The webinar will demonstrate how this can be done using simple formulas on an excel sheet itself and then introduce the time series function in IBM SPSS. A basic introduction to Box-Jenkins (ARIMA) modelling will also be covered.

A trial version of IBM SPSS version 20 can be downloaded from here. (You will need to fill in some details). Those interested in attending the seminar can drop an email at or fill in the form at  and we will mail you the webex invite. The webinar is scheduled on Sunday, 15th Jan, 0930 IST – 0400 GMT.

The webinar is free to attend.

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IBM SPSS Webinar on Youtube

For those who want to catch the webinar on IBM SPSS conducted on Jan 8th, we have uploaded the full 2 hour session on youtube. The audio can be a bit patchy in parts though.

The dataset used for the credit scoring exercise can be downloaded from here. The dataset with the validation report on deciles and cutoffs/profit calculation can be downloaded from here.

Do let us know your feedback in the comments section, for more information you can drop an email at