A.1 | The student is introduced to the types of time series and their uses. |
A.2 | The student can build a mathematical model of some problem. |
A.3 | The student should be able to use forecasting methods. |
A.4 | The student should be able to learn some special programs to conduct the necessary analyzes of the information system. |
In. (Mental skills)
B.1 | The student should link some time series and the possibility of using them to solve work-related problems. |
B.2 | The student should distinguish between the types of time series and how to deal with them. |
B.3 | Developing the student's ability to search and use the Internet. |
B.4 | The student should propose appropriate solutions to time series problems. |
c. (Practical & Professional Skills)
C.1 | The student acquires the skill of analyzing time series and how to predict them. |
C.2 | Depth in dealing with the concepts of time series and the application of these concepts in understanding the theories related to them. |
C.3 | Develop the skills of using computer programs in finding solutions to relevant issues and analyzing them. |
C.4 | Ability to self-read and solve problems at hand. |
W. (Generic and transferable skills)
D.1 | Ability to analyze and interpret results |
D.2 | The student should be able to use programming methods |
D.3 | Ability to communicate with colleagues |
D.4 | Ability to work as a team in solving certain statistical problems. |
Teaching and learning methods
Lectures
· Exercises
· Ready-made statistical programs
Methods of assessments
(Assessment table)
Rating No. | Evaluation methods | Evaluation Duration | Evaluation weight | Percentage | Rating Date (Week) | Reviews |
First Assessment | First exam | 3 | 20 | 20% | Sixth week |
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Second Assessment | Second exam | 3 | 20 | 20% | Twelfth week |
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Third Assessment | Practical exam | 4 | 10 | 10% | Week Fourteen |
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Final Evaluation | Final Exam | 4 | 50 | 50% | Commitment to the final schedule |
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Total | 100 degree | 100% | ||||
(Course contents )
Scientific topic | Number of Hours | Lecture | laboratory | Number of weeks |
Introduction to time series models and methods associated with data analysis and inference. | 15 | 9 | 6 | 3 |
Autoregression (AR), Moving Averages (MA), ARMA and ARIMA. | 15 | 9 | 6 | 3 |
Static and non-static processes, seasonal processes. | 15 | 9 | 6 | 3 |
Autocorrelation and partial autocorrelation functions. | 8 | 5 | 3 | 1.5 |
Define models and estimate parameters. | 5 | 3 | 2 | 1 |
Diagnostic examination of equipped models and forecasting. | 7 | 4 | 3 | 1.5 |
ARIMA Seasonal Time Series Forecasts and Models | 5 | 3 | 2 | 1 |
(References )
Bibliography | Publisher | Version | Author | Where it is located |
Rapporteur notes | Explanatory note prepared by the course instructor |
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Textbooks | Time Series Analysis with Applications in R. | 2nd | Cryer, J. and Chan, K. | Library
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Help Books |
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Scientific Journals | ||||
Internet Sites | ||||
Other |
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