İzmir Ekonomi Üniversitesi
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  • GRADUATE SCHOOL

    M.SC. In Industrial Engineering (With Thesis)

    STAT 557 | Course Introduction and Application Information

    Course Name
    Time Series Analysis
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    STAT 557
    Fall/Spring
    3
    0
    3
    7.5

    Prerequisites
    None
    Course Language
    English
    Course Type
    Elective
    Course Level
    Second Cycle
    Mode of Delivery -
    Teaching Methods and Techniques of the Course -
    National Occupation Classification -
    Course Coordinator -
    Course Lecturer(s)
    Assistant(s) -
    Course Objectives This course aims to make the students familiar with the basics of Time Series Analysis and its applications.
    Learning Outcomes

    The students who succeeded in this course;

    • will be able to determine if the given data is random.
    • will be able to solve difference equations.
    • will be able to analyze ARMA processes.
    • will be able to find estimators for parameters of ARMA processes.
    • will be able to work with filters.
    • will be able to constract linear regression models.
    • will be able to check hypotheses arising in Time Series Analysis.
    Course Description Different tests on randomness are discussed. Difference Equations and Lag Operators are considered. Analysis of ARMA processes is done. Moment estimators and Maximum Likelihood Estimatords are investigated. Forecasting for linear and nonlinear models is applied.

     



    Course Category

    Core Courses
    Major Area Courses
    Supportive Courses
    Media and Management Skills Courses
    Transferable Skill Courses

     

    WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

    Week Subjects Related Preparation Learning Outcome
    1 Introduction. Nonparametric tests on randomness “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893 pages: 414:445.
    2 Difference equations and lag operators “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893 pages: 1:35.
    3 MA processes “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893 pages: 43:50.
    4 AR and ARMA processes “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893 pages:53:62.
    5 Forecasting “Time Series Analysis” by J.D.Hamilton, Princeton, NJ.“Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893
    6 Maximum likelihood estimators for ARMA processes “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893 pages:117:132.
    7 Spectral analysis “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893 pages:152:170.
    8 Midterm exam
    9 Linear regression models “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893 pages:200:220.
    10 Covariance stationary vector processes “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893 pages:257:279.
    11 The Kalmam filter “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893 pages:372:394.
    12 Method of moments “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893 pages:409:424.
    13 Nonstationary time series “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893 pages: 435:447.
    14 Heteroscedasticity “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893 pages: 657:665.
    15 Semester review
    16 Final exam

     

    Course Notes/Textbooks

    “Time Series Analysis” by J. D. Hamilton, Primceton, NJ,1994. ISBN-13: 978-0691042893

    Suggested Readings/Materials

    ‘’New Introduction to Multiple Time Series Analysis” by H. Lutkepohl, Springer.2006. ISBN-13: 978-3540262398

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    2
    20
    Portfolio
    Homework / Assignments
    1
    10
    Presentation / Jury
    Project
    Seminar / Workshop
    Oral Exams
    Midterm
    1
    25
    Final Exam
    1
    45
    Total

    Weighting of Semester Activities on the Final Grade
    4
    55
    Weighting of End-of-Semester Activities on the Final Grade
    1
    45
    Total

    ECTS / WORKLOAD TABLE

    Semester Activities Number Duration (Hours) Workload
    Theoretical Course Hours
    (Including exam week: 16 x total hours)
    16
    3
    48
    Laboratory / Application Hours
    (Including exam week: '.16.' x total hours)
    16
    0
    Study Hours Out of Class
    14
    7
    98
    Field Work
    0
    Quizzes / Studio Critiques
    2
    5
    10
    Portfolio
    0
    Homework / Assignments
    1
    9
    9
    Presentation / Jury
    0
    Project
    0
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    1
    20
    20
    Final Exam
    1
    40
    40
        Total
    225

     

    COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

    #
    PC Sub Program Competencies/Outcomes
    * Contribution Level
    1
    2
    3
    4
    5
    1

    To have an appropriate knowledge of methodological and practical elements of the basic sciences and to be able to apply this knowledge in order to describe engineering-related problems in the context of industrial systems.

    -
    -
    -
    -
    -
    2

    To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment.

    -
    -
    -
    -
    -
    3

    To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality.

    -
    -
    -
    -
    -
    4

    To be able to conduct basic research and write and publish articles in related conferences and journals.

    -
    -
    -
    -
    -
    5

    To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results.

    -
    -
    -
    -
    -
    6

    To be able to manage decision-making processes in industrial systems.

    -
    -
    -
    -
    -
    7

    To have an aptitude for life-long learning; to be aware of new and upcoming applications in the field and to be able to learn them whenever necessary.

    -
    -
    -
    -
    -
    8

    To have the scientific and ethical values within the society in the collection, interpretation, dissemination, containment and use of the necessary technologies related to Industrial Engineering.

    -
    -
    -
    -
    -
    9

    To be able to design and implement studies based on theory, experiments and modeling; to be able to analyze and resolve the complex problems that arise in this process; to be able to prepare an original thesis that comply with Industrial Engineering criteria.

    -
    -
    -
    -
    -
    10

    To be able to follow information about Industrial Engineering in a foreign language; to be able to present the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.

    -
    -
    -
    -
    -

    *1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

     


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