DSO 424 – Business Forecasting Syllabus: Spring 2023
Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: daixieit
DSO 424 – Business Forecasting
Syllabus: Spring 2023
Course Description
Forecasting is a crucial tool in virtually every industry and business field, as it enables organizations to make informed decisions and plan for the future. This course is an in-depth exploration of hands-on time series forecasting using Python. Designed for business professionals looking to gain practical skills in time series forecasting, predictive analytics, data science, and big data, this course covers the essential techniques and tools required to build and use time series and machine learning models for forecasting future outcomes.Throughout the course, students will work on real-world business use cases, learning how to apply various time series and machine learning techniques to forecast future trends and outcomes. Key focus will be on data wrangling and preparation, which are critical to building accurate and effective forecasting models. Students will gain hands-on experience using Python libraries such as pandas, scikit-learn and statsmodels, as well as popular machine learning libraries such as sktime, pycaret, and if time permits Tensorflow and Keras. With the help of industry examples, students will learn how to model, evaluate, and interpret time series data and implement forecasting models in a business context. This course will equip students with the skills and knowledge required to analyze and forecast data in various business fields and industries. By the end of the course, students will be able to create robust forecasting models and make data-driven predictions with confidence.
Learning Objectives
● Understand the importance of forecasting in business and its applications in various industries
● Learn how to use Python and its libraries (pandas, scikit-learn, statsmodels, etc.) to analyze and model time series data
● Understand the different time series forecasting methods and their applications
● Learn how to wrangle and prepare data for forecasting models
● Gain hands-on experience building, evaluating, and interpreting time series forecasting models for business use cases
● Understand the importance of machine learning in time series forecasting and learn to use libraries like sktime, pycaret, Tensorflow and Keras for building advanced forecasting models
● Learn how to make data-driven predictions and decisions based on the forecasts obtained
Student Learning Outcomes:
● At the end of the course, students will be able to analyze any time series data using various statistical approaches
● Students will be able to generate reasonable forecast values using different forecasting models
● Students will be able to make decisions based on the forecasts obtained using data-driven predictions
● Students will be able to use Python and its libraries to wrangle, prepare and model time series data, apply the state of art models, and forecast future outcomes with confidence.
Required Materials
There is no required textbook. I will introduce the concepts and models in class. All lectures are recorded. Many course topics could be found in:
● Machine Learning for Time Series Forecasting with Python by Francesca Lazzari, 2021
● Principles of Business Forecasting, 2nd ed. by Keith Ord, Robert Fildes, Nikolaos Kourentzes, 2017
Technology
Good forecasting software is essential for a data scientist or business analyst. HW assignments, final project and competition could be done in any programming language, and you may use any libraries or software available to you. In this class we will use Python, Excel, and Orange.
Prerequisites
Any intro to statistics or data analysis course
Course Notes
We use Blackboard for all assignments, course materials, and announcements. Please check Blackboard and your email daily. If you would like hard copies of any course materials, it will be your responsibility to print them out prior to class.
Grading Policies
Grade components and weights are summarized below:
● HW assignments - 20% (groups up to 3 students)
● Midterm 25% (March 9)
● Final Team Competition- 15% (groups up to 3 students)
● Final Exam 40% (8am- 10am May 9)
Grades in this course will be assigned based on z-scores, with the goal of achieving a course GPA of 3.5. The cut-off points for grades will be determined by the instructor based on the overall performance of the class.
HW assignments
Regular homework assignments will be a central component of this course, providing an opportunity for students to apply their skills and knowledge to real-world business problems. These assignments will closely align with the case studies and examples explored in class, giving students a chance to experience the types of analytics challenges they may encounter in their first job. To further enhance the learning experience, students will have the option to collaborate in teams of 2-3 individuals on homework assignments, simulating a real-world work environment.
Competition
The course will feature a data science competition, where students will have the opportunity to showcase their skills and knowledge by working independently on a real-world data-driven problem. Detailed information about the competition and the grading criteria will be made available on the course's Blackboard page, providing students with a clear understanding of the expectations and objectives of the competition.
Exams
This course will feature two in-class exams, which will be open-book.
Assignment Submission Policy
All assignments must be turned in via Blackboard prior to the due date listed in this syllabus. For pre-class assignments, this is typically before the start of class. Specifically,
● Please plan ahead as the internet might break down unexpectedly if you wait until the last minute.
● Assignments are accepted through BB ONLY. Please DO NOT email me your assignments.
● Any assignment turned in late for any reason except medical emergency, even if by only a few minutes, will NOT be accepted.
MARSHALL GUIDELINES AND USC POLICIES
Students with Disabilities
USC is committed to making reasonable accommodations to assist individuals with disabilities in reaching their academic potential. If you have a disability which may impact your performance, attendance, or grades in this course and require accommodations, you must first register with the Office of Disability Services and Programs (www.usc.edu/disability). DSP provides certification for students with disabilities and helps arrange the relevant accommodations. Any student requesting academic accommodations based on a disability is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP.
DSP is located in GFS (Grace Ford Salvatori Hall) 120 and is open 8:30 a.m.–5:00 p.m., Monday through Friday. The phone number for DSP is (213) 740-0776. Email: [email protected].
Technical Support
The Help Desk is available to provide assistance 24 hours a day, every day. This assistance is restricted primarily to problems with the course delivery platform. Contact the Help Desk to resolve problems that you believe are not associated with the hardware and software you have purchased from a vendor. Examples include being unable to view multimedia files or view responses to comments you have posted in the discussion area. If you are not sure whether the problem is due to your computer system, contact the Help Desk for guidance; otherwise, contact the vendor. To talk to a live technical support agent, please call: 877-807-8557 or visit our Support Websitehttp://usc.echelp.org/.
USC Statement on Academic Conduct and Support Systems
Academic Conduct:
Plagiarism – presenting someone else’s ideas as your own, either verbatim or recast in your own words – is a serious academic offense with serious consequences. Please familiarize yourself with the discussion of plagiarism in SCampus in P B, Section 11, “Behavior Violating University Standards” policy.usc.edu/scampus-part-b. Other forms of academic dishonest are equally unacceptable. See additional information in SCampus and university policies on Research and Scholarship Misconduct.
Students with Disabilities:
USC welcomes students with disabilities into all of the University’s educational programs. The Office of Student Accessibility Services (OSAS) is responsible for the determination of appropriate accommodations for students who encounter disability-related barriers. Once a student has completed the OSAS process (registration, initial appointment, and submitted documentation) and accommodations are determined to be reasonable and appropriate, a Letter of Accommodation (LOA) will be available to generate for each course. The LOA must be given to each course instructor by the student and followed up with a discussion. This should be done as early in the semester as possible as accommodations are not retroactive. More information can be found at osas.usc.edu. You may contact OSAS at (213) 740-0776 or via email at [email protected] .
Support Systems:
Counseling and Mental Health - (213) 740-9355 – 24/7 on call
studenthealth.usc.edu/counseling
Free and confidential mental health treatment for students, including short-term psychotherapy, group counseling, stress fitness workshops, and crisis intervention.
National Suicide Prevention Lifeline - 1 (800) 273-8255 – 24/7 on call
Free and confidential emotional support to people in suicidal crisis or emotional distress 24 hours a day, 7 days a week.
Relationship and Sexual Violence Prevention Services (RSVP) - (213) 740-9355(WELL), press “0” after hours – 24/7 on call
studenthealth.usc.edu/sexual-assault
Free and confidential therapy services, workshops, and training for situations related to gender-based harm.
Office for Equity, Equal Opportunity, and Title IX (EEO- TIX) - (213) 740-5086
Information about how to get help or help someone affected by harassment or discrimination, rights of protected classes, reporting options, and additional resources for students, faculty, staff, visitors, and applicants.
Reporting Incidents of Bias or Harassment - (213) 740-5086 or (213) 821-8298
usc-advocate.symplicity.com/care_report
Avenue to report incidents of bias, hate crimes, and microaggressions to the Office for Equity, Equal Opportunity, and Title for appropriate investigation, supportive measures, and response.
The Office of StudentAccessibility Services (OSAS) - (213) 740-0776
OSAS ensures equal access for students with disabilities through providing academic accommodations and auxiliary aids in accordance with federal laws and university policy.
USC Campus Support and Intervention - (213) 821-4710
Assists students and families in resolving complex personal, financial, and academic issues adversely affecting their success as a student.
Diversity, Equity and Inclusion - (213) 740-2101
Information on events, programs and training, the Provost’s Diversity and Inclusion Council, Diversity Liaisons for each academic school, chronology, participation, and various resources for students.
USC Emergency - UPC: (213) 740-4321, HSC: (323) 442- 1000 – 24/7 on call
Emergency assistance and avenue to report a crime. Latest updates regarding safety, including ways in which instruction will be continued if an officially declared emergency makes travel to campus infeasible.
USC Department of Public Safety - UPC: (213) 740-6000, HSC: (323) 442- 120 – 24/7 on call
Non-emergency assistance or information.
Office of the Ombuds - (213) 821-9556 (UPC) / (323-442-0382 (HSC)
A safe and confidential place to share your USC-related issues with a University Ombuds who will work with you to explore options or paths to manage your concern.
Occupational Therapy Faculty Practice - (323) 442-3340 or otfp@med.usc.edu
Confidential Lifestyle Redesign services for USC students to support health promoting habits and routines that enhance quality of life and academic performance.
Emergency Preparedness/Course Continuity
In case of a declared emergency if travel to campus is not feasible, the USC Emergency Information web site (http://emergency.usc.edu/) will provide safety and other information, including electronic means by which instructors will conduct class using a combination of USC’s Blackboard learning management system (blackboard.usc.edu), teleconferencing, and other technologies.
TENTATIVE COURSE PLAN
COURSE TOPICS
Real life data sets will be to introduce each modeling technique
1. Examples of Business Data
2. Regression models with time series data:
o AB testing
o Time Series Data and the problem ofAutocorrelation
o Autocorrelation and the Durbin- Watson Test
o Solutions to Autocorrelation Problems
o Using Regression to Forecast Seasonal Data
3. New product forecasting
o Marketing research to aid new product forecasting
o Product life cycle
o Bass model
o Forecasting adoption of a new product (new generation product forecasting)
4. Moving averages and exponential smoothing:
o Forecasting Methods Based on Averaging
o Exponential Smoothing Adjusted for Trend: Holt’s Method
o Exponential Smoothing Adjusted for Trend and Seasonal Variation: Holt’s- Winter’s Method
5. Ensemble models: What to Do When One Model Isn’t Good Enough?
o Equal Weight Scenario
o Smaller Error – Larger Weight Scenario
o Optimal Linear Combinations
o Regression Approach
6. Classical time series decomposition:
o The Basic Multiplicative andAdditive Decomposition Models
o Deseasonalizing the Data and Finding Seasonal Indices
o Finding Long Term Trend
o Measuring the Cyclic Component, Time Series Decomposition Forecast
o Leading Indicators
7. Facebook’s model:
o Prophet
8. M3 forecasting competition winner:
o Theta model
9. ARIMA and VAR models:
o Time series forecasting using nonseasonal and seasonalARIMA models
10. Machine learning models:
o Time series forecasting using neural networks, boosted trees, advanced regression methods, deep learning etc.
11. Volatility models(if time permits):
o ARCH
o GARCG
o Applications
Note that this is just a skeleton of the course, and final class schedule, hours, dates may vary depending on the professor teaching the course and may be subject to change.
Open Expression and Respect for All
An important goal of the educational experience at USC Marshall is to be exposed to and discuss diverse, thought-provoking, and sometimes controversial ideas that challenge one’s beliefs. In this course we will support the values articulated in the USC Marshall “Open Expression Statement”
https://www.marshall.usc.edu/about/open-expression-statement
2023-03-09