Topics in Time Series Analysis: Old to New

During the fall 2019 professor Richard Davis, Columbia University, will give a course on Topics in Time Series Analysis: Old to New.

The course is aimed at advanced masters students and PhD students from Chalmers and Gothenburg University, and also welcomes students from other Scandinavian universities. The first meeting will be 

Monday, September 23, 13:15-15:00, room MVH 12 in the Mathematics Building, Chalmers tvärgata 3

During the meeting the detailed course schedule and content will be discussed and decided on. Preliminary plans are that it will be given in two or three one-week periods with around 3 or 4 two-hour lectures each of these weeks and with course weeks separated by one or two weeks without teaching. An overview of course contents follows below. We are investigating whether it will be possible to record the lectures. The course will give 5 ETCS points. 

Professor Davis has together with Peter Brockwell written two influential books on time series analysis and has published widely on time series analysis and extreme value theory. For more information see

If you want to follow the course, or have questions about it, please send a mail to Holger Rootzén,

Fall 2019
Richard A. Davis
Department of Statistics
Columbia University

The early development of time series focused almost exclusively on linear models, which still owns the core of the field. Nevertheless, there are many time series whose key features cannot be adequately captured by linear time series models. In this course, we will focus on these limitations and describe some of the important nonlinear and nonGaussian models that often provide a better description of time series data. Special attention will be given to financial time series and time series of counts. We will emphasize both the probabilistic and statistical aspects of these models. Additional topics, depending on the interest of the students and time constraints, include: high-dimensional time series, heavy-tailed time series, random matrix theory for heavy-tailed time series, independence testing using distance correlation, and structural break and outlier detection. 


Andersen, Torben Gustav, Richard A. Davis, Jens-Peter Kreiß, and Thomas V. Mikosch, eds. Handbook of financial time series. Springer Science & Business Media, 2009.

Brockwell, Peter J., Richard A. Davis. Time Series: Theory and Methods: Theory and Methods. Springer Science & Business Media, 1991. 

Cho, H., & Fryzlewicz, P. (2015). Multiple‐change‐point detection for high dimensional time series via sparsified binary segmentation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 77(2), 475-507.

Davis, R. A., Holan, S. H., Lund, R., & Ravishanker, N. (Eds.). (2016). Handbook of discrete-valued time series. CRC Press.

Davis, R. A., Heiny, J., Mikosch, T., & Xie, X. (2016). Extreme value analysis for the sample autocovariance matrices of heavy-tailed multivariate time series. Extremes, 19(3), 517-547.

Davis, R. A., Matsui, M., Mikosch, T., & Wan, P. (2018). Applications of distance correlation to time series. Bernoulli, 24(4A), 3087-3116.

Course Data
Type of schedule: 
Schedule to be decided
Credits (ECTS):