SF2943 Time Series Analysis 7.5 credits


  • Education cycle

    Second cycle
  • Main field of study

  • Grading scale

    A, B, C, D, E, FX, F

Course offerings

Spring 19 SAP for Study Abroad Programme (SAP)

  • Periods

    Spring 19 P4 (7.5 credits)

  • Application code


  • Start date


  • End date


  • Language of instruction


  • Campus

    KTH Campus

  • Tutoring time


  • Form of study


  • Number of places

    No limitation

  • Course responsible

    Pierre Nyquist <pierren@kth.se>

  • Teacher

    Pierre Nyquist <pierren@kth.se>

  • Target group

    Study Abroad Programme

Intended learning outcomes

To pass the course, the student should be able to do the following:

  • identify trends and seasonal variations in time series
  • define and calculate expectation, covariance function and spectral distribution and analyse their relations
  • estimate the above quantities for time series data and quantify the uncertainty in these estimates
  • predict real time series of different lengths, for instance by recursive methods
  • define and apply parametric models of ARMA type and analyse properties of the models
  • fit ARMA models to real data and select model order
  • explain the generalisations ARIMA and FARIMA of ARMA models
  • analyse data with parametric variance models of ARCH type
  • formulate models on state-space form, and describe Kalman filtering in generell terms

To receive the highest grade, the student should in addition be able to do the following:

  • Combine all the concepts and methods mentioned above in order to solve more complex problems.

Course main content

General introduction to time series. Stationary and non-stationary models, e.g. ARMA- and ARIMA-models. Projections and prediction of time series. Spectral theory. Estimation of parameters and spectra. Models on state-space form and Kalman filtering.


150 university credits (hp) whereas 45 university credits (hp) in mathematics. Including knowledge in Probability Theory and Statistics and Markov Processes (for example course SF1901 and SF1904) and documented proficiency in English corresponding to English B.

Advanced knowledge in Probability Theory is recommended (för example course SF2940).


Annonseras före kursstart på kurshemsidan.


  • OVN1 - Assignments, 3.0, grading scale: P, F
  • TENA - Examination, 4.5, grading scale: A, B, C, D, E, FX, F

Offered by



Pierre Nyquist (pierren@kth.se)


Pierre Nyquist <pierren@kth.se>

Supplementary information

Replaces SF2945.


Course syllabus valid from: Spring 2012.
Examination information valid from: Spring 2012.