FAUndergraduate courses
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.
- Intermediate
- 4 weeks
- Approx. 15 hours per week
- 3 semester hours
About this course
This course covers the forecasting methods most used in business: regression models, smoothing methods including moving average and exponential smoothing, and autoregressive models. It goes on to the enhancements that matter in practice — second-layer models and ensembles — and the problems that turn up once a forecast has to be produced every month rather than once.
Who this course is for
Data scientists, data analysts, sales forecasters, marketing managers, accountants, economists, financial analysts and risk managers — anyone who has to produce, interpret or judge a forecast.
What you will learn
9 outcomes
By the end of this course, you will be able 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 and exponential smoothing
- Adjust for seasonality
- Use regression methods for forecasting
- Account for autocorrelation
- Distinguish real trend and patterns from random behavior
Week by week
4-Week curriculum overview
This curriculum is identical across all start dates. Expand a week to explore the topics covered.
- Weeks
- 4
- Your week
- Approx. 15 hours
- Level
- Intermediate
Characterizing time series and the forecasting goal; evaluating predictive accuracy and data partitioningWeek 1
- Visualizing time series
- Time series components
- Forecasting versus explanation
- Performance evaluation
- Naive forecasts
Smoothing-based methodsWeek 2
- Model-driven versus data-driven methods
- Centered and trailing moving average
- Exponential smoothing — simple, double and triple
- De-trending and seasonal adjustment
- Differencing
Regression-based modelsWeek 3
- 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 models
Forecasting in practiceWeek 4
- Implementation issues — automation, managerial forecast adjustments and more
- Communicating forecasts to stakeholders
- Further methods — neural nets, ARIMA and logistic regression
- Forecasting binary outcomes
Instructors
Expert-Led Guidance
Each cohort is led by dedicated instructors and assistant teachers who actively lead weekly discussions, provide personalized feedback and grade your assignments.
Dr. Galit Shmueli
Dr. Galit Shmueli is a Distinguished Professor of the Institute of Service Science, College of Technology Management at National Tsing Hua University, Taiwan. Previous academic appointments include the SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics and Information Systems at the Indian School of Business, Hyderabad, and Associate Professor of Statistics in the Department of Decision, Operations & Information Technologies at the Smith School of Business, University of Maryland. Dr. Shmueli's research has been published in the statistics, information systems, and marketing literature.
Before you start
What you need to know first
An introductory statistics course
Course Format & Schedule
This is a 4-week, 100% online, asynchronous course.
- No mandatory live sessions: Log in and complete your work at times that fit your schedule.
- Weekly Releases: At the start of each week, you will receive new lecture materials and answer keys for the previous week's exercises.
- Interactive Community: Work through exercises, submit assignments, and engage with your instructor and peers via a private discussion board.
Homework
Short-answer questions testing the concepts, guided data analysis using software, and guided data modeling problems. There is also an end-of-course modeling project.
Texts
"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:
- Analytic Solver Data Mining (ASDM), previously 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 this software.
- R, a free statistical programming environment.
Be sure to choose the book that corresponds to your chosen software program.
For ASDM users: Course participants will receive a low-cost license for ASDM – this is a special version for this course. Do NOT download the free trial version of software from solver.com as it may conflict with the special course version.
Course dates
2026
09/12/2026to10/10/2026
Instructors: Dr. Galit Shmueli
10/05/2026to11/02/2026
Instructors: Dr. Galit Shmueli
10/08/2026to11/05/2026
Instructors: Dr. Galit Shmueli
Enrollment closed
FAQ
Do I need to know R to take this course?This course
Yes, familiarity with R programming is required if you choose to use it. Pick the edition of the text that matches whichever you choose.
Is this the same as a time series course?This course
It overlaps, but the emphasis is different. A time series course is usually about modeling what happened; this one is about producing a forecast somebody will act on, which is why evaluation, seasonality adjustment and the practical problems in week four take up as much room as the models themselves.
Is there a money-back or satisfaction guarantee?Every course
We handle cancellation and refund requests on an individual basis. If a course is not meeting your expectations or your circumstances change, please reach out to our team (support@learnstatistics.org) so we can work with you on a solution.
Can I transfer to a later start date or withdraw after the course begins?Every course
Yes. If you need to defer your enrollment to a future start date, withdraw mid-course, or transfer your seat to a colleague, please contact us directly. We address these requests flexibly on a case-by-case basis.
Who teaches the course?Every course
Each cohort is led by dedicated instructors and assistant teachers who actively lead weekly discussions, provide personalized feedback and grade your assignments.
Something not answered here? Ask us before you book — and the terms set out what a purchase covers.
Credit, and where it counts
This course carries a verified figure of 3 semester hours. A credit recommendation from the American Council on Education says what a course is worth; the institution receiving it decides whether to award it.
Anyone can take this subject: there is no application, and you do not have to be studying at a university. It also counts toward a degree at Thomas Edison State University, which decides what to award for it. The full position — including the limits each university publishes is on its own page, and worth reading before you buy.