Statistics 16:960:565 Applied Time Series Analysis


The course is normally offered during the Spring semester.
  • Class meeting dates: Please visit the University's academic calendar.
  • Schedule and Instructor: Please visit the University's schedule of classes for the instructor, time, and room.
  • Instructor and Teaching Assistant Office Hours: Please visit the Mathematical Finance program's office hour schedule.

Course Abstract

Model based forecasting methods, autoregressive and moving average models, ARIMA, ARMAX, ARCH, state-space models, estimation, forecasting and model validation, missing data, irregularly spaced time series, parametric and non-parametric bootstrap methods for time series, multi-resolution analysis of spatial and time series signals, time-varying models and wavelets.


Statistics 16:960:563 (Regression Analysis).

Primary Textbooks

Required: Brockwell, P., Davis, A. Introduction to Time Series and Forecasting . 2nd edition, Springer.
Suggested: Shumway, R., Stoffer, D. S., Time Series Analysis and Its Applications.
Suggested: Venables and Ripley , Modern Applied Statistics with Splus.


Please contact the instructor.

Class Policies

Please contact the instructor.

Previous Course Instructor Websites

Rebecka Jornsten

Weekly Lecturing Agenda and Readings

LectureTopicsReading Assignments
1 Introduction, Stationary Processes 1.1-1.6, 2.1-2.2
2 Stat. Processes, Measures of Dependence, Tests of Randomness 2.1-2.4, 1.4, 1.6
3 Forecasting 2.5,3.1-3.3
4 Spectral methods 4.1-4.4
5 Spectral methods 4.1-4.4
6 Review, Estimation 5.1-5.2
7 Estimation, Model selection. 5.2-5.5
8 ARIMA/ SARIMA 6.1-6.3
9 Multivariate 7.1-7.7
10 Multivariate, Bootstrap 7.1-7.7, handout
11 Smoothing, Image processing handout
12 Wavelets handout
13 State-Space 8.1-8.7
14 Wrap-up, new methods handout, 9.2

Library Reserves

All textbooks referenced on this page should be on reserve in the Hill Center Mathematical Sciences Library (1st floor). Please contact the instructor if reserve copies are insufficient or unavailable.