| Course Name |
(中) 商情預測(5126) |
| (Eng.) Business Forecasting |
| Offering Dept |
Department of Marketing |
| Course Type |
Elective |
Credits |
3 |
Teacher |
HSU,JANE LU |
| Department |
Department of Marketing/Undergraduate |
Language |
English |
Semester |
2026-FALL |
| Course Description |
This course introduces fundamental concepts and analytical methods for business forecasting, with an emphasis on data-driven decision making. Students will learn to identify data patterns, select and evaluate forecasting models, and interpret forecasting results using Excel, statistical software, and AI-assisted analytical tools. Topics include smoothing methods, regression-based forecasting, time-series models, forecast accuracy, and judgmental forecasting. |
| Prerequisites |
|
self-directed learning in the course |
Y |
| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
1. Develop fundamental analytical skills in business forecasting.
2. Apply and evaluate forecasting models for business decision making.
|
|
|
| Discussion |
| Lecturing |
| Exercises |
| Visit |
|
| Attendance |
| Assignment |
| Oral Presentation |
| Internship |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
09/07 Introduction to Business Forecasting: Data Patterns and Forecast Accuracy |
| Week 2 |
09/14 Moving Averages and Exponential Smoothing |
| Week 3 |
09/21 Trend and Seasonal Forecasting: Holt's and Holt-Winters Methods |
| Week 4 |
09/28 National Holiday (No Class) |
| Week 5 |
10/05 Team Forecasting Challenge: Smoothing Methods |
| Week 6 |
10/12 Simple Regression for Forecasting |
| Week 7 |
10/19 Multiple Regression for Forecasting |
| Week 8 |
10/26 National Holiday (No Class) |
| Week 9 |
11/02 Regression with Time-Series Data |
| Week 10 |
11/09 Field Trip: Business Forecasting in Practice |
| Week 11 |
11/16 Time-Series Analysis and Autocorrelation |
| Week 12 |
11/23 ARIMA: Concepts and Model Identification |
| Week 13 |
11/30 ARIMA: Estimation, Diagnostics, and Forecasting |
| Week 14 |
12/07 AI-Assisted Forecasting, Model Comparison, and Final Project Preparation |
| Week 15 |
12/14 Final Forecasting Project Presentations |
| Week 16 |
12/21 Final Project Revision and Submission |
self-directed
learning |
02.Viewing multimedia materials related to industry and academia.
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
1. Participation & In-class Activities (30%)
2. Team Forecasting Challenge (20%)
3. Field Trip Assignment (15%)
4. Final Project & Presentation (35%) |
| Textbook & other References |
Teaching Materials: Course materials, datasets, exercises, and supplementary readings provided by the instructor.
Reference: Hanke, J. E., & Wichern, D. W. (2014) Business Forecasting (9th ed.). Pearson. (ISBN: 9781292023007) |
| Teaching Aids & Teacher's Website |
|
| Office Hours |
Tuesday 14:00-16:00
Tel: (04)2284-0392 ext. 764
E-mail: jlu@dragon.nchu.edu.tw |
| Sustainable Development Goals, SDGs(Link URL) |
| include experience courses：N |
|