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.
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.
To pass the course, the student should be able to do the following:
To receive the highest grade, the student should in addition be able to do the following:
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Previous knowledge is assumed equivalent to SF1906 (5B1506).
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Brockwell and Davis: Introduction to Time Series and Forecasting, Springer-Verlag.
A, B, C, D, E, FX, 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.
Written examination.
Home works.
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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 SF2945Mathematics
Second cycle
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Replaced by SF2943 from 11/12.