Monday, 10 March 2014

Forecasting Analytics

"I have seen the future, and it is just like the present, only longer."  This quote from "The Profit" (a parody of "The Prophet") actually contains subtle insight into statistical forecasting.  It's not a magic wand - it is simply a collection of tools for projecting into the future what we think we know about how the present works.  Statistical forecasting is both an art and a science - the science part is covered in Dr. Galit Shmueli's "Forecasting Analytics," offered online at For more details please visit at

Aim of Course:
This course will teach you how to choose an appropriate time series forecasting method, fit the model, evaluate its performance, and use it for forecasting. The course will focus on the most popular business forecasting methods: Regression models, smoothing methods including Moving Average (MA) and Exponential Smoothing, and Autoregressive (AR) models. It will also discuss enhancements such as second-layer models and ensembles, and various issues encountered in practice.

Who Should Take This Course:
Business analysts, sales forecasters, economists, financial analysts, anyone who needs to produce, interpret or assess forecasts will find this course useful. Participants should be familiar with basic statistics, including linear regression.

Course Program:

Course outline: The course is structured as follows


Session 1: Characterizing Time Series and the Forecasting Goal; Evaluating Predictive Accuracy and Data Partitioning
  • Visualizing time series
  • Time series components
  • Forecasting vs. explanation
  • Performance evaluation
  • Naive forecasts

Session 2: Regression-Based Models
  • Overview of forecasting methods
  • Capturing trend seasonality and irregular patterns with linear regression
  • Measuring and interpreting autocorrelation
  • Evaluating predictability and the Random Walk
  • Second-layer models using Autoregressive (AR) models

Session 3: Smoothing-Based Methods
  • Model-driven vs. data-driven methods
  • Centered and training Moving Average (MA)
  • Exponential Smoothing (simple, double, triple)
  • De-trending and seasonal adjustment
  • Differencing


Session 4: Forecasting in Practice
  • Forecasting implementation issues (automation, managerial forecast adjustments, and more)
  • Communicating forecasts to stakeholders
  • Overview of further forecasting methods (neural nets, ARIMA, and logistic regression)
  • Forecasting binary outcomes

The instructor, Dr. Galit Shmueli, Professor of Data Analytics at the Indian School of Business (SRITNE) in Hyderabad, is a co-author of "Data Mining for Business Intelligence," "Practical Time Series Forecasting," and "Statistical Methods in e-Commerce Research."

You will be able to ask questions and exchange comments with the Dr. Galit Shmueli via a private discussion board throughout the course.   The courses take place online at in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants accepts registration for its courses at special reduced prices in Indian Rupees through us, the Center for eLearning and Training (C-eLT), Pune.

For India Registration and pricing, please visit us at

Call: 020 6680 0322


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