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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025

Biomedical Data Analysis and Processing

The course is not on the list Without time-table
Code Completion Credits Range Language
F7ABBAZD KZ 2 1P+1C English
Garant předmětu:
Lecturer:
Tutor:
Supervisor:
Department of Biomedical Informatics
Synopsis:

Time series analysis, trends, mutual dependency, stationarity. Correlation function and covariance function. Algorithms of correlation function estimation. Impact of removing trends to autocorrelation function. Periodogram - relationship between corellogram and periodogram. Frequency spectrum, spectrum of random signals. Linear frequency filtering. AR, ARMA, and MA processes. Spectral analysis. FFT algorithm. Non-parametric methods of the frequency spectrum estimation. Positives and negatives of the specteal analysis. Repeated measurements and analysis of their properties. AR a ARMA model parameter identification. Prediction. Bivariance analysis of time series - cross-correlation and cross-covariance and their estimation. Bispectrum.

Requirements:

- tutorials: 2 control test

- examination: written test

Syllabus of lectures:

1. Time series analysis - fundamentals; trends, mutual dependency, stationarity. Correlation function and covariance function. Algorithms of correlation function estimation.

2. Impact of removing trends to autocorrelation function. Periodogram - relationship between corellogram and periodogram.

3. Frequency spectrum, spectrum of random signals. Linear frequency filtering.

4. AR, ARMA, and MA processes. Spectral analysis. FFT algorithm.

5. Non-parametric methods of the frequency spectrum estimation. Positives and negatives of the spectral analysis.

6. Repeated measurements and analysis of their properties.11. AR and ARMA model parameter identification.

7. Prediction. Bivariance analysis of time series - cross-correlation and cross-covariance. Estimation of cross-correlation and cross-covariance functions. Bispectrum.

Syllabus of tutorials:

1. Time series filtering (MA), time series decomposition.

2. Box-Jenkinson methodology.

3. Control test (25 points), interpolation and time series processing.

4. Classification task.

5. Cluster analysis.

6. Implementation of the fuzzy approximator.

7. Control test (25 points).

Study Objective:

to provide students with basic methods of statistical processing time series typical for life sciences

Study materials:

[1]Diggle P.J. Time Series. A Biostatistical Introduction. Clarendon Press. Oxford 1996

[2]Weiss S.M., Indurkhya N. Predictive Data Mining

Note:
Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2024-06-16
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