TS539 Applied Time Series Analysis

6 ECTS - 3-0 Duration (T+A)- 1. Semester- 3 National Credit
Information About Course

Information
Goal, Content & Resources
Course Learning Outcomes
Relation with Program Learning Outcome
Week Plan
Student Workload - ECTS

Information

Unit INSTITUTE OF NATURAL AND APPLIED SCIENCES
AGRICULTURAL STRUCTURES AND IRRIGATION (MASTER) (WITH THESIS)
Code TS539
Name Applied Time Series Analysis
Term 2018-2019 Academic Year
Term Fall
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 Instructor
The current term course schedule has not been prepared yet.


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

Resources

Notes



Course Learning Outcomes

Order Course Learning Outcomes
LO01 Learns time series concept comprehensively.
LO02 Comprehends structural behavior of any kind of time series, and set out the mothematical model.
LO03 Sets out the stochastic proces and their autocorrelation structures, and interprest the results.
LO04 Learns how to apply goodness-of-fit tests for the models; Gains ability how to generates synthetic data by using adapted models.
LO05 Models time series components and gains skills/abilities to establish a unique integrated time series model.


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.
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.
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
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
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
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
5 Structural behaviors of time series: Trend, intermittency, periodicity and stocasticity (CONT.) 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
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
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
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
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
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
11 Stochastic process and and serial dependency: Lag concept, ACF, PACF, spectral analysis (CONT.) 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
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
13 AR(p) models with constant and periodical parameters: Model description, principles of parsimony in parametes, parameter estimation (CONT.) 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
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
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
16 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
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


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: 05.05.2025 02:49