| Course Description |
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| Course Name |
: |
Applied Time Series Analysis |
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| Course Code |
: |
TS-539 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Fall (16 Weeks) |
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| ECTS |
: |
6 |
|
| Name of Lecturer(s) |
: |
Prof.Dr. MAHMUT ÇETİN
|
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| Learning Outcomes of the Course |
: |
1. Learns time series concept comprehensively.
2. Comprehends structural behavior of any kind of time series, and set out the mothematical model.
3. Sets out the stochastic proces and their autocorrelation structures, and interprest the results.
4. Learns how to apply goodness-of-fit tests for the models; Gains ability how to generates synthetic data by using adapted models.
|
|
| Mode of Delivery |
: |
Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
Objectives are to:
1. Acquire skills on time series modelling issue,
2. Model hydrologic and hydrometeorological time series.
3. Interpret the results in depth. |
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| Course Contents |
: |
Definitions, terms and notations. Elementary statistical principles in time series analysis. Step-by-step sequential analysis of structural characteristics: Tendency, intermittency, periodicity, and stochasticity. Trend analysis. Estimation of periodic parameters by Fourier analysis. Removing trend and periodic component from stochastic process. Time dependence structure: Autocorrelation and partial autoorrelation function for lag k. Spectral analysis. Autoregressive modelling [AR(p)] with constant and/or periodic parameters: Preliminary analysis and model identification, the principle of parsimony in parameters, parameter estimation, goodness of fit tests for selected model. Reliability of model parameters. Random number generators and synthetic data generation. Simple ARIMA modelling of time series: Parameter estimation, goodness of fit tests and synthetic data generation.
|
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| Language of Instruction |
: |
Turkish |
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| Work Place |
: |
Class |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Time series and process concept, basic definitions, notations |
Books and other study materials |
Self-study plus lecturing |
|
2 |
Important remindings on statistical inference, descriptive statistics, and interpretation of statistics |
Books and other study materials |
Self-study plus lecturing |
|
3 |
Pre-statistical analyssi in time series modelling issue |
Books and other study materials |
Self-study plus lecturing |
|
4 |
Structural behaviors of time series: Trend, intermittency, periodicity and stocasticity |
Books and other study materials |
Self-study plus lecturing |
|
5 |
Structural behaviors of time series: Trend, intermittency, periodicity and stocasticity (CONT.) |
Books and other study materials |
Self-study plus lecturing |
|
6 |
Diognising trend component and its modelling |
Books and other study materials |
Self-study plus lecturing |
|
7 |
Analysis of periodic component: Fourier approach |
Books and other study materials |
Self-study plus lecturing |
|
8 |
Removing trend and periodicity from experimental data |
Books and other study materials |
Self-study plus lecturing |
|
9 |
Stochastic process and and serial dependency: Lag concept, ACF, PACF, spectral analysis |
Books and other study materials |
Self-study plus lecturing |
|
10 |
Mid-term exam |
Books and other study materials |
Take-home exam |
|
11 |
Stochastic process and and serial dependency: Lag concept, ACF, PACF, spectral analysis (CONT.) |
Books and other study materials |
Self-study plus lecturing |
|
12 |
AR(p) models with constant and periodical parameters: Model description, principles of parsimony in parametes, parameter estimation |
Books and other study materials |
Self-study plus lecturing |
|
13 |
AR(p) models with constant and periodical parameters: Model description, principles of parsimony in parametes, parameter estimation (CONT.) |
Books and other study materials |
Self-study plus lecturing |
|
14 |
Model parameters and confidence tests: Random number generation techniques, AR(p) models, parameter estimation and goodnes-of-fit tests. |
Books and other study materials |
Self-study plus lecturing |
|
15 |
Synthetic data generation with integrated models: Up-to-date practices |
Books and other study materials |
Self-study plus lecturing |
|
16/17 |
Final exam |
Books and other study materials |
Take-home exam |
|
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Has the ability to develop and deepen the level of expertise degree qualifications based on the knowledge acquired in the field of agriculture and irrigation structures |
3 |
|
2 |
Has the ability to understand the interaction between irrigation and agricultural structures and related disciplines |
2 |
|
3 |
Qualified in devising projects in agricultural structures and irrigation systems. |
1 |
|
4 |
Conducts land applications,supervises them and assures of development |
3 |
|
5 |
Has the ability to apply theoretical and practical knowledge in the field of agricultural structures and irrigation department |
5 |
|
6 |
Has the ability to support his specilist knowledge with qualitative and quantitative data. Can work in different disciplines. |
5 |
|
7 |
Solves problems by establishing cause and effect relationship |
5 |
|
8 |
Able to carry out a study independently on a subject. |
4 |
|
9 |
Has the ability to design and apply analytical, modelling and experimental researches, to analyze and interpret complex issues occuring in these processes.
|
4 |
|
10 |
Can access resources on his speciality, makes good use of them and updates his knowledge constantly. |
3 |
|
11 |
Has the ability to use computer software in agricultural structures and irrigation; can use informatics and communications technology at an advanced level.
|
3 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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