DEGREE PROGRAMS  Associate's Degree (Short Cycle)  Bachelor’s Degree (First Cycle)  Master’s Degree (Second Cycle)

Course Description Course Name : Time series analysis Course Code : İSB411 Course Type : Optional Level of Course : First Cycle Year of Study : 4 Course Semester : Fall (16 Weeks) ECTS : 5 Name of Lecturer(s) : Assoc.Prof.Dr. MAHMUDE REVAN ÖZKALE Learning Outcomes of the Course : Distinguish the components of the time series Comment the time series graphics Apply the decomposition methods Determine the regression model that fits the data Distinguish the difference between smoothing techniques Explain the statistical basics of Box Jenkins models Distinguish between the Box Jenkins models that fit the time series data Apply the necessary methods for time-series forecasting and prediction Use the statistical package programs necessary for time series analysis Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : Time series modeling, forecasting and prediction, and the use of a variety of package programs related to them Course Contents : The components of the time series, the time series graphics, the decomposition methods, the regression models in time series, exponential smoothing techniques, Box-Jenkins Models, the statistical package programs Language of Instruction : Turkish Work Place : Faculty of Arts and Sciences Annex Classrooms   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Interpretation of time series and time-series graphics Source reading Lecture and examples 2 Autocorrelation and partial autocorrelation functions Source reading Lecture and examples 3 Examination of stationary Source reading Lecture and examples 4 Portmanteau tests, the index numbers Source reading Lecture and examples 5 Decomposition methods Source reading Lecture, using statistical package program 6 Introduction to time series regression analysis, normality tests, the problem of heteroscedasticity Source reading Lecture 7 autocorrelation test, regression analysis in non-seasonal time series Source reading Lecture, using statistical package program 8 Regression analysis in non-seasonal time series Source reading Lecture, using statistical package program 9 Midterm exam Review the topics discussed in the lecture notes and sources Written exam 10 Regression analysis in seasonal tiem series Source reading Lecture, using statistical package program 11 Exponential smoothing methods Source reading Lecture, problem-solving, using statistical package program 12 Autoregression (AR) models and properties Source reading Lecture, problem-solving, using statistical package program 13 Moving average (MA) models and properties Source reading Lecture, problem-solving, using statistical package program 14 ARIMA models, parameter estimation Source reading Lecture, using statistical package program 15 Dickey-Fuller unit root test Source reading Lecture 16/17 Final exam Review the topics discussed in the lecture notes and sources Written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  1. Kadılar, C. (2005), SPSS Uygulamalı Zaman Serileri Analizine Giriş. Bizim Büro Basımevi 2. Sevüktekin, M., Nargeleçekenler, M. (2005), Zaman Serileri Analizi. Nobel Yayın Dağıtım 3. Cryer, J. D. (1986), Time Series Analysis. PWS-KENT Publishing Company Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 80     Homeworks/Projects/Others 1 20 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Utilize computer systems and softwares 0 2 Apply the statistical analyze methods 5 3 Make statistical inference(estimation, hypothesis tests etc.) 1 4 Generate solutions for the problems in other disciplines by using statistical techniques 4 5 Discover the visual, database and web programming techniques and posses the ability of writing programme 0 6 Construct a model and analyze it by using statistical packages 5 7 Distinguish the difference between the statistical methods 3 8 Be aware of the interaction between the disciplines related to statistics 3 9 Make oral and visual presentation for the results of statistical methods 4 10 Have capability on effective and productive work in a group and individually 2 11 Develop scientific and ethical values in the fields of statistics-and scientific data collection 4 12 Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics 1 13 Emphasize the importance of Statistics in life 5 14 Define basic principles and concepts in the field of Law and Economics 0 15 Produce numeric and statistical solutions in order to overcome the problems 4 16 Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events 4 17 Use proper methods and techniques to gather and/or to arrange the data 5 18 Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs 0 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 3 42 Assesment Related Works     Homeworks, Projects, Others 1 20 20     Mid-term Exams (Written, Oral, etc.) 1 12 12     Final Exam 1 20 20 Total Workload: 136 Total Workload / 25 (h): 5.44 ECTS Credit: 5