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Time Series Analysis (Summer Semester 2016)

Lecture: Tuesday 14:00-15:30 SR 2.59
Problem class: Wednesday 14:00-15:30 SR 2.58
Friday 14:00-15:30 SCC-PC-Pool L
Lecturer Prof. Dr. Tilmann Gneiting
Office hours: by appointment
Room 2.019 Kollegiengebäude Mathematik (20.30)
Email: tilmann.gneiting@kit.edu
Problem classes Dipl.-Math. oec. Markus Scholz
Office hours: Tuesday, 10:30-11:30 o'clock
Room 2.010 Kollegiengebäude Mathematik (20.30)
Email: ma.scholz@kit.edu


A time series is a set of data $\{ x_t \}$ in which the subscript $t$
indicates the time at which the datum $x_t$ was observed. The course
provides an introduction to the theory and practice of statistical
time series analysis. Topics covered include stationary and
non-stationary stochastic processes, autoregressive and moving average
(ARMA) models, state-space models and Kalman filter, model selection
and estimation, forecasting and forecast assessment, and an outline
of spectral techniques.

Statistical Software for Time Series Data

The problem sets will frequently require the use of a suitable
statistical programming language. Any code discussed in class
meetings will be written in the R language, and you are encouraged to
use R, or your standard language if it is suitable.

ILIAS Course

Course Material and important information can be found in the ILIAS course of the lecture.


There will be oral examinations towards the end of the semester. All further details can be found in the ILIAS course of the lecture.


  • Brockwell, P. J. and Davis, A. (2006). Time Series, Theory and

Methods, second edition. New York: Springer.

  • Brockwell, P. J. and Davis, A. (2010). Introduction to Time

Series and Forecasting, second edition. New York: Springer.

  • Chatfield, C. (2004). The Analysis of Time Series, an

Introduction, sixth edition. London: Chapman & Hall/CRC.

  • Diggle, P. J. (2004). Time Series, a Biostatistical

Introduction. Oxford: Clarendon Press.

  • Shumway, R. H. and Stoffer, D. S. (2011). Time Series Analysis

and Its Applications: With R Examples, third edition. New York:

  • Venables, W. N. and Ripley, B. D. (2004). Modern Applied

Statistics with S-PLUS, fourth edition. New York: Springer.

  • Wei, W. W. S. (2006). Time Series Analysis, Univariate and

Multivariate Methods, second edition. Boston: Pearson Addison

  • Zivot, E. and Wang, J. (2006). Modeling Financial Time Series

With S-Plus, second edition. New York: Springer.