SF2945 Time Series Analysis 6.0 credits

Tidsserieanalys

The overall purpose of the course is that the student should be well acquainted with basic concepts, theory, models and solution methods in time series analysis, models for "dependent" data. Such data are common in economical (e.g., the price development of a product) and in natural science (e.g., meteorological observations) applications.

Offering and execution

Course offering missing for current semester as well as for previous and coming semesters

Course information

Content and learning outcomes

Course contents *

General introduction to time series. Stationary and non-stationary models, e.g. ARMA- and ARIMA-models. Prediction of time series. Spectral theory. Estimation of parameters and of the spectra. Filtering.

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 mentioned quantities for time series data and quantify the uncertainty in these estimates
  • predict on real time series of different lengths, for instance by recursive methods
  • define and apply parametric models of ARMA type and analyse the properties of the models
  • fit ARMA models to real data
  • explain the generalisations ARIMA and FARIMA of ARMA models
  • analyse data with parametric variance models of ARCH type
  • describe in a broad sense Kalman filters

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 Disposition

No information inserted

Literature and preparations

Specific prerequisites *

Previous knowledge is assumed equivalent to SF1906 (5B1506).

Recommended prerequisites

No information inserted

Equipment

No information inserted

Literature

Brockwell and Davis: Introduction to Time Series and Forecasting, Springer-Verlag.

Examination and completion

Grading scale *

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

Examination *

  • TEN1 - Examination, 4.5 credits, Grading scale: A, B, C, D, E, FX, F
  • ÖVN1 - Assignments, 1.5 credits, Grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade *

Written examination.
Home works.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

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Examiner

No information inserted

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web SF2945

Offered by

SCI/Mathematics

Main field of study *

Mathematics

Education cycle *

Second cycle

Add-on studies

No information inserted

Ethical approach *

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Supplementary information

Replaced by SF2943 from 11/12.