TÜRKÇE
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Information
| Code | IEM745 |
| Name | Applied Time Series Analysis I |
| Term | 2023-2024 Academic Year |
| Term | Fall and Spring |
| 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 | Dr. Öğr. Üyesi FELA ÖZBEY |
| Course Instructor |
1 |
Course Goal / Objective
To introduce some methods used in time series analysis and their applications in R programming language.
Course Content
Characteristics of Time Series; Time Series Regression and Exploratory Data Analysis; ARIMA Models; Spectral Analysis and Filtering.
Course Precondition
None
Resources
Jonathan D. Cryer , Kung-Sik Chan ( 2008), Time Series Analysis with Applications in R Second Edition, Springer, ISBN: 978-0-387-75958-6
Notes
James Douglas Hamilton, (1994) Time Series Analysis, Princeton University Press, ISBN: 9780691042893
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Specifies time series relations. |
| LO02 | Chooses the most appropriate model for time series data. |
| LO03 | Estimates time series models. |
| LO04 | Codes techniques and models taught in this course. |
| LO05 | Uses R programming language fluently. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explains contemporary concepts about Econometrics, Statistics, and Operation Research | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Explains relationships between acquired knowledge about Econometrics, Statistics, and Operation Research | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Explains how to apply acquired knowledge in the field to Economics, Business, and other social sciences | 4 |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Performs conceptual analysis to develop solutions to problems | 3 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Models problems with Mathematics, Statistics, and Econometrics | 3 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Interprets the results obtained from the most appropriate method to predict the model | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Synthesizes the information obtained by using different sources within the framework of academic rules in a field that does not research | 3 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Uses acquired knowledge in the field to determine the vision, aim, and goals for an organization/institution | |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Searches for new approaches and methods to solve problems being faced | 2 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Presents analysis results conveniently | 2 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Collects/analyzes data in a purposeful way | 4 |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Converts its findings into a master's thesis or a professional report in Turkish or a foreign language | |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Develops solutions for organizations using Econometrics, Statistics, and Operation Research | |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research | 3 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads by taking responsibility individually and/or within the team | |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the necessity of lifelong learning, it constantly renews itself by following the current developments in the field of study | |
| PLO17 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses a package program of Econometrics, Statistics, and Operation Research or writes a new code | 5 |
| PLO18 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Interprets the feelings, thoughts and behaviors of the related persons correctly/expresses himself/herself correctly in written and verbal form | |
| PLO19 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets data on economic and social events by following current issues | |
| PLO20 | Yetkinlikler - Alana Özgü Yetkinlik | Applies social, scientific and professional ethical values |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Characteristics of Time Series: Introduction; The Nature of Time Series Data; Time Series Statistical Models; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 2 | Characteristics of Time Series: Measures of Dependence: Autocorrelation and Cross-Correlation; Stationary Time Series; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 3 | Characteristics of Time Series: Estimation of Correlation; Vector-Valued and Multidimensional Series; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 4 | Time Series Regression and Exploratory Data Analysis: Introduction; Classical Regression in the Time Series Context; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 5 | Time Series Regression and Exploratory Data Analysis: Exploratory Data Analysis; Smoothing in the Time Series; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 6 | ARIMA Models: Introduction; Autoregressive Moving Average Models; Difference Equations; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 7 | ARIMA Models: Autocorrelation and Partial Autocorrelation; Forecasting; Estimation; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Ödev |
|
| 9 | ARIMA Models: Integrated Models for Nonstationary Data; Building ARIMA Models; Multiplicative Seasonal ARIMA Models; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 10 | Spectral Analysis and Filtering: Introduction; Cyclical Behavior and Periodicity; The Spectral Density; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 11 | Spectral Analysis and Filtering: Periodogram and Discrete Fourier Transform; Nonparametric Spectral Estimation; Parametric Spectral Estimation; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 12 | Spectral Analysis and Filtering: Multiple Series and Cross-Spectra; Linear Filters; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 13 | Spectral Analysis and Filtering: Dynamic Fourier Analysis and Wavelets; Lagged Regression Models; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 14 | Spectral Analysis and Filtering: Signal Extraction and Optimum Filtering; Spectral Analysis of Multidimensional Series; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma |
| 15 | Applications on Some Time Series | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
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 | ||