Skip to content
Forecasting Analytics

Forecasting Analytics

This course will teach you how to choose an appropriate time series model: fit the model, conduct diagnostics, and use the model for forecasting.


Complex sample designs such as stratified cluster sampling make it possible to extract maximum information at minimum cost, but they are typically harder to work with than simple random samples. How do you analyze the resulting data – in particular, how do you determine margins of error? This course teaches you how to estimate variances when analyzing survey data from complex samples, and also how to fit linear and logistic regression models to complex sample survey data.

  • Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

This class teaches students how to:

  • 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

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.

Our Instructors

Course Syllabus

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


Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

Frequently Asked Questions

  • What is your satisfaction guarantee and how does it work?

  • Can I transfer or withdraw from a course?

  • Who are the instructors at

Visit our knowledge base and learn more.

Register For This Course

Forecasting Analytics

Additional Information

Time Requirements

About 15 hours per week, at times of your choosing.


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.

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.


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 low-cost license for XLMiner – this is a special version, for this course. Do NOT download the free trial version of XLMiner from as it may conflict with the special course version.

[testimonial_view id=”17″ post_ids=”15447″]

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

Forecasting Analytics