FMSN45 course code for LTH
MASM17 course code for NF

General Information

Elective for: BME4-sbh, C4, D4-ssr, E4-ss, F4, F4-bg, F4-bm, F4-fm, F4-r, F4-ss, F4-mai, I4-fir, Pi4-fm, Pi4-ssr, Pi4-biek, Pi4-bam, MMSR2, R4 and Master Mathematical Statistics
Language of instruction: The course will be given in English

Contents

Time series analysis concerns the mathematical modelling of time varying phenomena, e.g., ocean waves, water levels in lakes and rivers, demand for electrical power, radar signals, muscular reactions, ECG-signals, or option prices at the stock market. The structure of the model is chosen both with regard to the physical knowledge of the process, as well as using observed data. Central problems are the properties of different models and their prediction ability, estimation of the model parameters, and the model's ability to accurately describe the data. Consideration must be given to both the need for fast calculations and to the presence of measurement errors. The course gives a comprehensive presentation of stochastic models and methods in time series analysis. Time series problems appear in many subjects and knowledge from the course is used in, i.a., automatic control, signal processing, and econometrics.

Further studies of ARMA-processes. Non-stationary models, slowly decreasing dependence. Transformations. Optimal prediction and reconstruction of processes. State representation, principle of orthogonality, and Kalman filtering. Parameter estimation: Least squares and Maximum likelihood methods as well as recursive and adaptive variants. Non-parametric methods,covariance estimation, spectral estimation. An orientation on robust methods and detection of outliers.

Examination details

LTH:

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Written and oral project presentation, and compulsory computer exercises. The final grade is based on the project with bonus assignments for higher grade.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Parts
Code: 0120. Name: Computer Work 1.
Credits: 0,5. Grading scale: UG. Assessment: Computer exercise 1
Code: 0220. Name: Computer Work 2.
Credits: 0,5. Grading scale: UG. Assessment: Computer exercise 2 och 3
Code: 0320. Name: Project Work and Home Exam.

NF:

2101 Project and take home exam, 6,5 hp
Grading scale: Fail, Pass, Pass with distinction
2103 Laboratory Work part 1, 0,5 hp
Grading scale: Fail, Pass
2104 Laboratory Work part 2, 0,5 hp
Grading scale: Fail, Pass

Admission requirements

LTH:

FMSF10 Stationary Stochastic Processes or FMSF20 Mathematical Statistics, Basic Course or FMSF25 Mathematical Statistics - Complementary Project or FMSF32 Mathematical Statistics or FMSF45 Mathematical Statistics, Basic Course or FMSF50 Mathematical Statistics, Basic Course or FMSF55 Mathematical Statistics, Basic Course or FMSF70 Mathematical Statistics or FMSF75 Mathematical Statistics, Basic Course or FMSF80 Mathematical Statistics, Basic Course

NF:

For admission to the course knowledge equivalent to the course MASC04, Stationary
Stochastic processes, 7.5 credits is required together with English B.

Reading list

Andreas Jakobsson: An Introduction to Time Series Modeling. Studentlitteratur, 2019, ISBN: 9789144134031. Minor differences with previous editions.

Contact and other information

Director of studies: studierektor@matstat.lu.se

Syllabus

Kursplan LTH (SV)

Kursplan NF (SV)

Syllabus LTH (EN)

Syllabus NF (EN)