MODULE HANDBOOK
Module name Time Series Analysis
Module level, if applicable Bachelor Code, if applicable SST-505 Subtitle, if applicable -
Courses, if applicable Time Series Analysis Semester(s) in which the
module is taught 5th (fifth) Person responsible for the
module Chair of lab. Data Mining
Lecturer Arum Handini Primandari, S.Pd.Si., M.Sc.
Mujiati Dwi Kartikasari, S.Si., M.Sc.
Language Bahasa Indonesia
Relation to curriculum Compulsory course in the third year (5th semester) Bachelor Degree Type of teaching, contact
hours 100 minutes lectures and 120 minutes structured activities per week.
Workload
Total workload is 90.67 hours per semester, which consists of 100 minutes lectures per week for 14 weeks, 120 minutes structured activities per week, 120 minutes individual study per week, in total is 16 weeks per semester, including mid exam and final exam.
Credit points 2
Requirements according to the examination regulations
Students have taken Time Series Analysis course (SST-505) and have an examination card where the course is stated on.
Recommended prerequisites Students have taken Applied Regression Analysis (SST-305).
Module objectives/intended learning outcomes
After completing this course, the students have ability to:
CO 1. master basic strategies for transferring methods in the case of time series data
CO 2. do simple scientific assignments and are able to present the results well in the case of time series data
Content
The basic concept of time series: forecasting, forecasting data and methods, basic step in forecasting, time series data, time series pattern.
Deterministic time series models and methods: moving average, exponential smoothing, decomposition, fuzzy time series.
Stochastic time series models and methods: AR, MA, ARMA, ARIMA, SARIMA.
Study and examination requirements and forms of examination
The final mark will be weighted as follows:
No Assessment components Weight (percentage)
1 CO 1 80%
2 CO 2 20%
Media employed White-board, Laptop, LCD Projector
Reading list
1. Hyndman, R.J., & Athanasopoulos, G. 2018. Forecasting:
principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2.
2. Montgomery, Douglas C., et al., 2015, Introduction to Time Series Analysis and Forecasting 2nd Ed, Wiley: Canada.
3. Makridakis, 2008, Forecasting: method and application 3rd Ed, Wiley
Mapping CO, PLO, and ASIIN’s SSC
ASIIN PLO
E N T H U S I A S T I C
Knowledge a b c d Ability e f
Competency g
h CO1
i j k
l CO2
