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Εικόνα επιλογής

Time Series Analysis - Erasmus

(6145) -  IOANNIS OIKONOMIDIS

Περιγραφή Μαθήματος

Introduction

The course introduces the basic concepts of time series analysis, covering both the theoretical background and practical applications using R. The course comprises the following chapters:

  1. Introduction and Unit-Root Testing
    Basic time series concepts, weak stationarity, autocovariance and autocorrelation functions, random walks, trends, differencing, order of integration, and Dickey–Fuller/ADF tests.

  2. Stationary ARMA Models
    White noise, MA, AR and ARMA models, moment properties, ACF/PACF, the lag operator, and conditions for stationarity and invertibility.

  3. The Box–Jenkins Framework
    Identification, estimation using least squares and maximum likelihood, model selection, residual diagnostics, and forecasting.

  4. ARMA Modeling Applications
    A complete empirical analysis workflow in R: data import and transformation, graphical exploration, estimation, comparison, diagnostic checking, and forecast evaluation.

  5. Time Series Models of Heteroscedasticity
    Characteristics of financial returns, ARCH tests, ARCH, GARCH and EGARCH models, estimation, diagnostic checking, and volatility forecasting.

  6. Non-Stationary Time Series Models
    Spurious regression, cointegration, the Engle–Granger procedure, an overview of the Johansen approach, and error-correction models.

  7. Assignment Presentations and Summary
    Application of the overall methodology in an individual project, presentation and documentation of results, integration of the material, and review.

Study Material

The lecture slides are uploaded in the Documents folder of eClass. Additional study resources for the course are listed below:

  1. Tsay, Ruey S., Time Series Analysis, 3rd Edition, Tziola Publications, 2024.

  2. Zivot, Eric, and Wang, Jiahui, Modeling Financial Time Series with S-PLUS, Springer Science and Business Media Inc., 2006.

  3. Shumway, Robert H., and Stoffer, David S., Time Series Analysis and Its Applications, Springer Science and Business Media LLC, 2006.

  4. Gilgen, Hans, Univariate Time Series in Geosciences, Springer-Verlag Berlin Heidelberg, 2006.

  5. Kirchgässner, Gebhard, and Wolters, Jürgen, Introduction to Modern Time Series Analysis, Springer-Verlag Berlin Heidelberg, 2007.

  6. Brockwell, P. J., and Davis, R. A., Introduction to Time Series and Forecasting, 3rd Edition, Springer-Verlag, 2016.

  7. Cryer, J. D., and Chan, K. S., Time Series Analysis With Applications in R, Springer-Verlag, 2008.

Course Assessment

Students have the option to complete a course assignment that can enhance their final grade. The assignment includes an analysis in R and a brief presentation of the results. It is individual and optional. More details will be given when we reach Chapter 4 of the course.

The final grade is the maximum of the following two:
A. final examination (100%).

B. final examination (80%) + written assignment (20%). 

 

Contact

You can contact the instructor by email at goikon@aueb.gr and schedule a meeting during office hours on Wednesdays, 11:00–13:00, at the Statistics Lab (Main Building, Antoniadou Wing, 3rd floor).

Ημερομηνία δημιουργίας

Δευτέρα, 5 Οκτωβρίου 2026

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