Information Package / Course Catalogue
| Big Data Analytics (English) (Non-Thesis) | |||||
| Master | Length of the Programme: 1.5 | Number of Credits: 90 | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF: Level 7 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Gradutate School of Science and Engineering | ||||
| Course Code | BDA 543 | ||||
| Course Title in English | Time Series Forecasting | ||||
| Course Title in Turkish | Zaman Serisi Tahminleme | ||||
| Language of Instruction | EN | ||||
| Type of Course | Flipped Classroom | ||||
| Level of Course | Intermediate | ||||
| Semester | Spring | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 172 hours per semester | ||||
| Number of Credits | 7.5 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | Basic knowledge of • probability distributions, expectation, variance • linear regression, logistic regression | ||||
| Co-requisites | None | ||||
| Registration Restrictions | Only Graduate Students | ||||
| Overall Educational Objective | To learn the basic methods and models for time series forecasting with hands-on applications. | ||||
| Course Description | This course reviews the fundamental methods and models that are used in time series forecasting. It presents the basic properties of time series data such as stationarity, seasonality and trend. It covers common forecasting methods and models including smoothing methods, ARIMA models, proportional hazard models, GARCH models, VARs. It also introduces various machine learning techniques that are applied in time series forecasting. | ||||
| Course Description in Turkish | Bu ders temel zaman serisi tahminleme metotlarını ve modellerini gözden geçirir. Durağan süreç, mevsimsellik, eğilim gibi temel zaman serisi kavramlarını tanıtır. Yumuşatma metotları, ARIMA, orantılı hazard modelleri, GARCH modelleri, vektör otoregresyon modellerin üstünden geçer. Ayrıca zaman serisi tahminlemede kullanılan çeşitli makine öğrenmesi tekniklerini tanıtır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Understand the basic concepts of time series data such as stationarity, trend, seasonality, and autocorrelation 2) Determine and treat the components of a time series data 3) Determine appropriate methods or models to solve a time series forecasting problem 4) Apply the forecasting procedure and interpret the results |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 1) Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. | ||||
| 2) Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. | ||||
| 3) Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. | ||||
| 4) Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. | ||||
| 5) Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. | ||||
| 6) Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. | ||||
| 7) Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. | ||||
| 8) Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. | ||||
| 9) Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. | ||||
| 10) Understanding of social and environmental aspects of machine learning applications. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. | N | |
| 2) | Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. | N | |
| 3) | Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. | N | |
| 4) | Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. | N | |
| 5) | Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. | N | |
| 6) | Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. | N | |
| 7) | Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. | N | |
| 8) | Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. | N | |
| 9) | Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. | N | |
| 10) | Understanding of social and environmental aspects of machine learning applications. | N |
| Prepared by and Date | , |
| Course Coordinator | ÖZGÜR ÖZLÜK |
| Semester | Spring |
| Name of Instructor |
Course Contents
| Week | Subject |
| 1) | • Introduction to time series data • Introduction to time series forecasting • Stationarity, Seasonality, Trend • Review of regression methods (if time permits) |
| 2) | • Introduction to time series data • Introduction to time series forecasting • Stationarity, Seasonality, Trend • Review of regression methods (if time permits) |
| 3) | Smoothing Methods |
| 4) | Smoothing Methods |
| 5) | • ARMA, ARIMA, SARIMA models • Autocorrelation |
| 6) | • ARMA, ARIMA, SARIMA models • Autocorrelation |
| 7) | • Survival Analysis • Kaplan-Meier Curve • Proportional Hazard Models |
| 8) | • Survival Analysis • Kaplan-Meier Curve • Proportional Hazard Models |
| 9) | ARCH, GARCH models |
| 10) | ARCH, GARCH models |
| 11) | VAR Models |
| 12) | VAR Models |
| 13) | • Machine Learning Applications • Artificial Neural Networks, Boosting methods etc. |
| 14) | • Machine Learning Applications • Artificial Neural Networks, Boosting methods etc. |
| 15) | Final Examination Period |
| 16) | Final Examination Period |
| Required/Recommended Readings | None | |||||||||
| Teaching Methods | Flipped classroom/Exercise/Laboratory/Active learning | |||||||||
| Homework and Projects | Students will be given 5 assignments: one assignment each week from week #2 to week #5. Each assignment will include numerical applications of the methods or models that will be taught in class. Students will have one week to submit an assignment. | |||||||||
| Laboratory Work | Students will apply the methods they learned using a statistical computation program. | |||||||||
| Computer Use | Students will apply the methods they learned using a statistical computation program. | |||||||||
| Other Activities | None | |||||||||
| Assessment Methods |
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| Course Administration |
02123953600 |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 2 | 1.5 | 1 | 63 | ||
| Laboratory | 14 | 2 | 1.5 | 1 | 63 | ||
| Homework Assignments | 5 | 12 | 60 | ||||
| Total Workload | 186 | ||||||
| Total Workload/25 | 7.4 | ||||||
| ECTS | 7.5 | ||||||