Time series (ΣΕΕ11)

• Ελληνική Έκδοση
• Graduate Courses Outlines
• Outline Modification (available only for faculty members)
• Department of Mathematics
• Save as PDF or Print (to save as PDF, pick the corresponding option from the list of printers, located in the window which will popup)

General

School	School of Science
Academic Unit	Department of Mathematics
Level of Studies	Graduate
Course Code	ΣΣΕ11
Semester	2
Course Title	Time series
Independent Teaching Activities	Lectures (Weekly Teaching Hours: 3, Credits: 7.5)
Course Type	Specialized general knowledge
Prerequisite Courses	-
Language of Instruction and Examinations	Greek
Is the Course Offered to Erasmus Students	Yes (in English)
Course Website (URL)	See eCourse, the Learning Management System maintained by the University of Ioannina.

Learning Outcomes

Learning outcomes	Upon completion of this course, a student will: be familiar with properties of the major types of time series be able to identify appropriate models for time series. be able to diagnose model adequacy. construct time series models from data and verify model fits use statistical packages to construct time series models and conduct analysis
General Competences	Working independently Decision-making Adapting to new situations Production of free, creative and inductive thinking Synthesis of data and information, with the use of the necessary technology Working in an interdisciplinary environment

Syllabus

Introduction to stationary time series. Simple models for time series. Linear processes, general autoregressive-moving average models. Prediction of stationary time series. The families of ARMA, ARIMA and State space models. Seasonality in time series. Modelling stochastic volatility. Time series regression. Nonlinear non-Gaussian time series. Multivariate time series. Multivariate autoregressive model.

Teaching and Learning Methods - Evaluation

Delivery	Face-to-face
Use of Information and Communications Technology	Statistical Software Use of ICT in communication with students
Teaching Methods	Activity Semester Workload Lectures 39 Working independently 70 Study and analysis of bibliography, Fieldwork 78.5 Course total 187.5
Student Performance Evaluation	LANGUAGE OF EVALUATION: Greek METHODS OF EVALUATION: written work (20%), Final exam (80%)

Attached Bibliography

• Shumway, R.H. and Stoffer, D.S. (2017) Time Series Analysis and Its Applications with R Examples, 4rd edition, Springer-Verlag, New York.
• Brockwell, P.J. and R. A.. Davis (2016) Introduction to Time Series and Forecasting, 3nd edition, Springer-Verlag, New York.
• Cowpertwait, P.S.P. and A.V. Metcalfe (2009) Introductory Time Series with R, Spinger-Verlag.
• Cryer, J.D. and K-S Chan (2010) Time Series Analysis: with applications in R, 2nd Edition, Springer
• Δημέλη Σ. (2003, 3η Έκδοση): Σύγχρονες Μέθοδοι Ανάλυσης Χρονολογικών Σειρών, Εκδόσεις ΚΡΙΤΙΚΗ, Αθήνα.
• Θαλασσινός Λευτέρης Ι. Ανάλυση χρονολογικών σειρών Μεθοδολογία Box-Jenkins.
• [Περιοδικό / Journal] Journal of Time Series Analysis
• [Περιοδικό / Journal] Journal of Time Series Econometrics