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.
Overview
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
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.
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
Class Dates
2023
Instructors: Dr. Galit Shmueli
2024
Instructors: Dr. Galit Shmueli
Instructors: Dr. Galit Shmueli
Instructors: Dr. Galit Shmueli
2025
Instructors: Dr. Galit Shmueli
Instructors: Dr. Galit Shmueli
Instructors: Dr. Galit Shmueli
Prerequisites
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Susan Kamp
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Stephen McAllister
I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.
Amir Aminimanizani
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Richard Jackson
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Additional Information
Organization of Course
This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.
At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.
Time Requirements
This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.
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.
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 low-cost license for XLMiner – this is a special version, for this course. Do NOT download the free trial version of XLMiner from solver.com as it may conflict with the special course version.
Course Fee & Information
Enrollment
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.
Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.
Group Rates
Contact us to get information on group rates.
Discounts
Academic affiliation? In most courses you are eligible for a discount at checkout.
New to Statistics.com? Click here for a special introductory discount code.
Invoice or Purchase Order
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.
Options for Credit and Recognition
This course is eligible for the following credit and recognition options:
No Credit
You may take this course without pursuing credit or a record of completion.
Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.
CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.
ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for college credit. For recommendation details (level, and number of credits), please see this page. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.
ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.
INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.
Supplemental Information
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