Module Handbook-Mathematics-Universitas Brawijaya
Module Handbook
Module Name: Introduction to Forecasting Methods
Module Level: Bachelor
Abbreviation, if Applicable:
MAM62405 Sub-Heading, if
Applicable:
- Courses included in the module, if applicable
Introduction to Forecasting Methods Semester/term: 4th/2nd year
Module Coordinator(s): Chair of the Lab. Industrial and Finance Mathematics Lecturer(s) Drs. Imam Nurhadi Purwanto, M.T.
Mila Kurniawaty, S.Si., M.Si.
Language Indonesian
Classification within the curriculum
Elective Studies Teaching format / class
hours per week during semester:
100 minutes lectures per week.
Workload: Total workload is 3 ECTS, which consists of 1.67 hours lectures, 2 hours structured activities, 2 hours independent learning, 16 week per semester, and a total 90.67 hours per semester including mid exam and final exam.
Credit Points: 2
Requirements according to the examination regulations:
Students have taken Introduction to Forecasting Methods course (MAM62405), have attendance at least 80%, and have the examination card when where the course is stated on.
Recommended prerequisties
Students have taken Introduction to Linear Regression course (MAM62402) and have participated in the final exam on the module.
Learning
goals/competencies or
Module
objectives/intended learning outcomes
After completing this course the student should have
CLO 1 : Ability to understand the basic concepts of Forecasting.
CLO 2 : Ability to understand the basic concepts of Quantitative Forecasting.
CLO 3 : Ability to use Smoothing Methods.
CLO 4 : Ability to use Time Series Methods.
CLO 5 : Ability to apply forecasting methods based on the objectives and characteristics, also interpret the results.
Content: 1. Introduction to Forecasting : Need and uses of forecasting, Current Status of Forecasting Techniques, The future of forecasting.
2. Fundamentals of Quantitative Forecasting : Explanatory vs Time Series Forecasting and Least Square Estimates.
3. Smoothing Methods: Averaging methods Exponential Smoothing Methods.
Module Handbook-Mathematics-Universitas Brawijaya 4. Time Series Methods: Fundamental of Time Series Analysis, The
Box-Jenkins Methods.
5. Apply forecasting methods in several fields.
Study / exam achievements:
The final mark will be weighted as follows:
No. Assessment methods (component, activities). Weight
1. Assigment 10 %
2. Quiz 25 %
3. Mid examination 30 %
4. Final examination 35 %
Final grades is defined as follow: A : 80 < Final Mark ≤ 100 B+ : 75 < Final Mark ≤ 80
B : 69 < Final Mark ≤ 75
C+ : 60 < Final Mark ≤ 69
C : 55 < Final Mark ≤ 60
D+ : 50 < Final Mark ≤ 55
D : 44 < Final Mark ≤ 50
E : 0 ≤ Final Mark ≤ 44
Forms of Media Slides and LCD projectors, laptop/ computer, whiteboards.
Learning Methods Lecture
Literature 1. Makridakis, S., Wheelwright, SC. dan McGee, VE., 1983, Forecasting: Methods and Applications, second edition, John Wiley & Sons, New York.
2. Chatfield, C. 1984. The Analysis of Time Series an Introduction.
Chapman and Hall, New York.
3. Cryer, J.D. dan SikChan, K. 2008. Time Series Analysis with Application in R. Springer, Iowa.
4. Ledolter, J. dan Abraham, B. 1983. Statistical Method to Forecasting. John Wiley & Sons, New York.
5. Harris, R dan Robert S.2003. Applied Time Series Modelling and Forecasting. John Wiley & Sons, England.
6. Wei, W.S., 1994. Time Series.Analysis. Univariate and Multivariate Method. AddisonWesley. Pub. Company, New York.
Notes:
