Forecasting Analytics

Forecasting Analytics

taught by Galit Shmueli

 

 
Close Popup

Aim of Course:

In this online course, “Forecasting Analytics,” you will learn 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.

Anticipated learning outcomes:

  • Visualize time series data
  • Understand the different components of time series data
  • Distinguish explanation from forecasting
  • Specify appropriate metrics to assess forecasting models
  • Use smoothing methods with time series data (moving average, exponential smoothing)
  • Adjust for seasonality
  • Use regression methods for forecasting
  • Account for autocorrelation
  • Distinguish real trend and patterns from random behavior

 

This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

WEEK 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

WEEK 2: Smoothing-Based Methods

  • Model-driven vs. data-driven methods
  • Centered and trailing Moving Average (MA)
  • Exponential Smoothing (simple, double, triple)
  • De-trending and seasonal adjustment
  • Differencing

 

WEEK 3: 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

WEEK 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

HOMEWORK:

Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software and guided data modeling problems using software.

In addition to assigned readings, this course also has an end of course data modeling project.

Forecasting Analytics

Who Should Take This Course:
Data Scientists, data analysts, sales forecasters, marketing managers, accountants, economists, financial analysts, risk managers, anyone who needs to produce, interpret or assess forecasts will find this course useful. Participants should be familiar with basic statistics, including linear regression.
Level:
Intermediate
Prerequisite:
Organization of the Course:
Options for Credit and Recognition:
Course Text:

"Practical Time Series Forecasting" in eBook or hardcopy, or, if you are using R, "Practical Time Series Forecasting in R."  Those in South Asia can purchase the books online here.

Software:

This is a hands-on course, and, while any software capable of doing time series forecasting can be used, assignment support is offered for two programs:

1. XLMiner, a data mining program available either (a) for Windows versions of Excel or (b) over the web. Course participants will have access to a low-cost license for XLMiner.

2. R, a free statistical programming environment.

Be sure to choose the book that corresponds to your chosen software program.

 

For XLMiner users:  Course participants will have receive a no-cost license for XLMiner - this is a special version, for this course.  IMPORTANT:  Do NOT download the free trial version of XLMiner from solver.com (it may conflict with the special course version). 

Instructor(s):

Dates:

July 12, 2019 to August 09, 2019 November 15, 2019 to December 13, 2019 March 13, 2020 to April 10, 2020 July 10, 2020 to August 07, 2020 November 13, 2020 to December 11, 2020 March 12, 2021 to April 09, 2021 July 09, 2021 to August 06, 2021 November 12, 2021 to December 10, 2021

Forecasting Analytics

Instructor(s):

Dates:
July 12, 2019 to August 09, 2019 November 15, 2019 to December 13, 2019 March 13, 2020 to April 10, 2020 July 10, 2020 to August 07, 2020 November 13, 2020 to December 11, 2020 March 12, 2021 to April 09, 2021 July 09, 2021 to August 06, 2021 November 12, 2021 to December 10, 2021

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

Register Now

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).

Contact Us
Have a question about a course before you register? Call us. We're here for you. (571) 281-8817 or ourcourses (at) statistics.com

Want to be notified of future courses?

Yes
Student comments