TS539 Applied Time Series Analysis

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code TS539
Name Applied Time Series Analysis
Term 2024-2025 Academic Year
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Yüksek Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. MAHMUT ÇETİN


Course Goal / Objective

Objectives of this course are three-fold: a) to acquire skills on time series modelling issue, b) to model hydrologic and hydrometeorological time series, c) to interpret the results in depth.

Course Content

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.

Course Precondition

In order to enroll this course, it is sufficient to be a graduate or doctoral student. There are no other prerequisites.

Resources

1. Kottegoda, N. T., 1980. Stochastic Water Resources Technology. Department of Civil Engineering, University of Birmingham, UK, Softcover ISBN: 978-1-349-03469-7, eBook ISBN: 978-1-349-03467-3, DOI: https://doi.org/10.1007/978-1-349-03467-3. 2. Machiwal, D., Jha, M., K., 2012. Hydrologic Time Series Analysis. DOI: https://doi.org/10.1007/978-94-007-1861-6, pp. 280, eBook ISBN: 978-94-007-1861-6. 3. Salas, J. D., Delleur, J. W., Yevjevich, V., Lane, W. L., 1997. Applied Modelling of Hydrologic Time Series. Water research publication, Littleton, CO, USA.

Notes

Publications such as scientific articles, MSc or PhD theses, reports, etc. written by using hydrologic and hydro-meterologic time series.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Learns time series concept comprehensively.
LO02 Models Mathematically the structural behavior of time series.
LO03 Interprets stochastic process and their autocorrelation structures.
LO04 Gains the ability to generate synthetic time series from different models by applying goodness-of-fit tests for the model.
LO05 Constructs the integrated time series model integrally by modeling the time series components seperately.
LO06 Gains the ability to model the structural behavior of time series mathematically.
LO07 Interprets the coefficients in the mathematical models of time series.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal 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
PLO02 Bilgi - Kuramsal, Olgusal Has the ability to understand the interaction between irrigation and agricultural structures and related disciplines
PLO03 Bilgi - Kuramsal, Olgusal Qualified in devising projects in agricultural structures and irrigation systems. 1
PLO04 Bilgi - Kuramsal, Olgusal Conducts land applications,supervises them and assures of development
PLO05 Bilgi - Kuramsal, Olgusal Has the ability to support his specilist knowledge with qualitative and quantitative data. Can work in different disciplines. 2
PLO06 Bilgi - Kuramsal, Olgusal Solves problems by establishing cause and effect relationship 4
PLO07 Bilgi - Kuramsal, Olgusal Has the ability to apply theoretical and practical knowledge in the field of agricultural structures and irrigation department 5
PLO08 Bilgi - Kuramsal, Olgusal Able to carry out a study independently on a subject.
PLO09 Bilgi - Kuramsal, Olgusal Has the ability to design and apply analytical, modelling and experimental researches, to analyze and interpret complex issues occuring in these processes.
PLO10 Beceriler - Bilişsel, Uygulamalı Can access resources on his speciality, makes good use of them and updates his knowledge constantly. 3
PLO11 Yetkinlikler - Öğrenme Yetkinliği Has the ability to use computer software in agricultural structures and irrigation; can use informatics and communications technology at an advanced level. 5


Week Plan

Week Topic Preparation Methods
1 Time series and process concept, basic definitions, notations Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
2 Important remindings on statistical inference, descriptive statistics, and interpretation of statistics Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
3 Pre-statistical analyssi in time series modelling issue Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
4 Structural behaviors of time series: Trend, intermittency, periodicity and stocasticity Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
5 Applications of trend, intermittency, periodicity and stocasticity to real time series data sets. Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Anlatım, Proje Temelli Öğrenme
6 Diognising trend component and its modelling Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
7 Analysis of periodic component: Fourier approach Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Anlatım, Tartışma, Alıştırma ve Uygulama
8 Mid-Term Exam Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Ölçme Yöntemleri:
Yazılı Sınav
9 Removing trend and periodicity from experimental data Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
10 Stochastic process and and serial dependency: Lag concept, ACF, PACF, spectral analysis Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Anlatım, Tartışma
11 Application of lag concept, ACF, PACF, spectral analysis to real hydrologic and hydro-meteorologic data sets. Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Tartışma, Proje Temelli Öğrenme , Soru-Cevap
12 AR(p) models with constant and periodical parameters: Model description, principles of parsimony in parametes, parameter estimation Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
13 AR(p) and ARIMA (p,q) autoregressive models with periodic parameters: Model definition, parsimony principle in model parameter count, parameter estimation Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
14 Model parameters and confidence tests: Random number generation techniques, AR(p) models, parameter estimation and goodnes-of-fit tests. Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
15 Synthetic data generation with integrated models: Up-to-date practices Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Öğretim Yöntemleri:
Tartışma, Proje Temelli Öğrenme
16 Term Exam Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Ölçme Yöntemleri:
Yazılı Sınav, Proje / Tasarım
17 Term Exams Studying in detail the relevant chapters in different sources such as books, reports or scientific papers; reviewing and reading articles related to the subject from scientific journals Ölçme Yöntemleri:
Yazılı Sınav, Proje / Tasarım


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 13.05.2024 02:03