Bi6446 Time Series Forecasting
Faculty of ScienceSpring 2022
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. Ing. Jiří Holčík, CSc.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Epidemiology and modeling (programme PřF, N-MBB)
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series prediction not only with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics - Learning outcomes
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics. - Syllabus
- 1. Why prediction usually fails.
- 2. Prediction – what is it?, preliminary analysis, transformation & adjustments, prediction models - method of simple forecasting.
- 3. Prediction models – regression, linear prediction (autoregressive models, moving average models).
- 4. Prediction models – linear prediction (exponential smoothing).
- 5. Judgemental forecasting.
- 6. Forecasting evaluation
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Time Series Forecasting
Faculty of Scienceautumn 2021
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. Ing. Jiří Holčík, CSc.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Epidemiology and modeling (programme PřF, N-MBB)
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series prediction not only with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics - Learning outcomes
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics. - Syllabus
- 1. Why prediction usually fails.
- 2. Prediction – what is it?, preliminary analysis, transformation & adjustments, prediction models - method of simple forecasting.
- 3. Prediction models – regression, linear prediction (autoregressive models, moving average models).
- 4. Prediction models – linear prediction (exponential smoothing).
- 5. Judgemental forecasting.
- 6. Forecasting evaluation
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Time Series Forecasting
Faculty of ScienceSpring 2021
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. Ing. Jiří Holčík, CSc.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Epidemiology and modeling (programme PřF, N-MBB)
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series prediction not only with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics - Learning outcomes
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics. - Syllabus
- 1. Why prediction usually fails.
- 2. Prediction – what is it?, preliminary analysis, transformation & adjustments, prediction models - method of simple forecasting.
- 3. Prediction models – regression, linear prediction (autoregressive models, moving average models).
- 4. Prediction models – linear prediction (exponential smoothing).
- 5. Judgemental forecasting.
- 6. Forecasting evaluation
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Time Series Forecasting
Faculty of ScienceSpring 2020
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Prerequisites (in Czech)
- Bi5440 Time series
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Epidemiology and modeling (programme PřF, N-MBB)
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series prediction not only with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics - Learning outcomes
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics. - Syllabus
- 1. Why prediction usually fails.
- 2. Prediction – what is it?, preliminary analysis, transformation & adjustments, prediction models - method of simple forecasting.
- 3. Prediction models – regression, linear prediction (autoregressive models, moving average models).
- 4. Prediction models – linear prediction (exponential smoothing).
- 5. Judgemental forecasting.
- 6. Forecasting evaluation
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Spectral Analysis of Time Series
Faculty of ScienceSpring 2019
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Timetable
- Mon 18. 2. to Fri 17. 5. Tue 9:00–11:50 MP2,01014a
- Prerequisites (in Czech)
- Bi5440 Time series
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design modified algorithms to process data of given particular characteristics
- Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ... 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences. 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation. 4. Spectral analysis algorithms for regularly and irregularly sampled signals. 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods. 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models. 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy. 8. Burg method. Unconstrained Least-Squares Method for AR model parameters. 9. Properties of methods for AR models, their comparison. Selection of AR-model order. 10. ARMA and MA models for power spectrum estimation 11. Sequential estimation methods 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method 13. Prony methods
- Literature
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- IEEE Signal Processing Letters
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- IEEE Trans. on Signal Processing
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Spectral Analysis of Time Series
Faculty of Sciencespring 2018
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Timetable
- Tue 11:00–13:50 MP2,01014a
- Prerequisites (in Czech)
- Bi5440 Time series
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design modified algorithms to process data of given particular characteristics
- Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ... 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences. 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation. 4. Spectral analysis algorithms for regularly and irregularly sampled signals. 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods. 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models. 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy. 8. Burg method. Unconstrained Least-Squares Method for AR model parameters. 9. Properties of methods for AR models, their comparison. Selection of AR-model order. 10. ARMA and MA models for power spectrum estimation 11. Sequential estimation methods 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method 13. Prony methods
- Literature
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- IEEE Signal Processing Letters
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- IEEE Trans. on Signal Processing
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Spectral Analysis of Time Series
Faculty of ScienceSpring 2017
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Timetable
- Mon 20. 2. to Mon 22. 5. Mon 9:00–11:50 MP2,01014a
- Prerequisites (in Czech)
- Bi5440 Time series
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics - Learning outcomes
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics. - Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ...
- 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences.
- 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation.
- 4. Spectral analysis algorithms for regularly and irregularly sampled signals.
- 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods.
- 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models.
- 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy.
- 8. Burg method. Unconstrained Least-Squares Method for AR model parameters.
- 9. Properties of methods for AR models, their comparison. Selection of AR-model order.
- 10. ARMA and MA models for power spectrum estimation
- 11. Sequential estimation methods
- 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method
- 13. Prony methods
- Literature
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- IEEE Signal Processing Letters
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- IEEE Trans. on Signal Processing
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer. At practical exercises results of solving problems assigned as homeworks are disscused.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Spectral Analysis of Time Series
Faculty of ScienceSpring 2016
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Prerequisites (in Czech)
- Bi5440 Signals & Linear Systems
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design modified algorithms to process data of given particular characteristics
- Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ... 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences. 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation. 4. Spectral analysis algorithms for regularly and irregularly sampled signals. 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods. 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models. 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy. 8. Burg method. Unconstrained Least-Squares Method for AR model parameters. 9. Properties of methods for AR models, their comparison. Selection of AR-model order. 10. ARMA and MA models for power spectrum estimation 11. Sequential estimation methods 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method 13. Prony methods
- Literature
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- IEEE Signal Processing Letters
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- IEEE Trans. on Signal Processing
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Spectral Analysis of Time Series
Faculty of ScienceSpring 2015
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Timetable
- Tue 10:00–11:50 MP2,01014a, Tue 12:00–12:50 MP2,01014a
- Prerequisites (in Czech)
- Bi5440 Signals & Linear Systems
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design modified algorithms to process data of given particular characteristics
- Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ... 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences. 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation. 4. Spectral analysis algorithms for regularly and irregularly sampled signals. 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods. 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models. 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy. 8. Burg method. Unconstrained Least-Squares Method for AR model parameters. 9. Properties of methods for AR models, their comparison. Selection of AR-model order. 10. ARMA and MA models for power spectrum estimation 11. Sequential estimation methods 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method 13. Prony methods
- Literature
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- IEEE Signal Processing Letters
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- IEEE Trans. on Signal Processing
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.
Information on course enrolment limitations: základy metod zpracování signálů a spektrální analýzy
Bi6446 Spectral Analysis of Time Series
Faculty of ScienceSpring 2014
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
doc. Ing. Daniel Schwarz, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Prerequisites (in Czech)
- Bi5440 Signals & Linear Systems
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design modified algorithms to process data of given particular characteristics
- Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ... 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences. 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation. 4. Spectral analysis algorithms for regularly and irregularly sampled signals. 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods. 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models. 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy. 8. Burg method. Unconstrained Least-Squares Method for AR model parameters. 9. Properties of methods for AR models, their comparison. Selection of AR-model order. 10. ARMA and MA models for power spectrum estimation 11. Sequential estimation methods 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method 13. Prony methods
- Literature
- IEEE Trans. on Signal Processing
- IEEE Signal Processing Letters
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
Information on course enrolment limitations: základy metod zpracování signálů a spektrální analýzy
Bi6446 Spectral Analysis of Time Series
Faculty of ScienceSpring 2013
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
doc. Ing. Daniel Schwarz, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Timetable
- Wed 15:00–16:50 MP2,01014a, Wed 17:00–17:50 MP2,01014a
- Prerequisites (in Czech)
- Bi5440 Signals & Linear Systems
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-EXB)
- Course objectives
- At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design of modified algorithms to process data of given particular characteristics
- Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ... 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences. 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation. 4. Spectral analysis algorithms for regularly and irregularly sampled signals. 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods. 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models. 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy. 8. Burg method. Unconstrained Least-Squares Method for AR model parameters. 9. Properties of methods for AR models, their comparison. Selection of AR-model order. 10. ARMA and MA models for power spectrum estimation 11. Sequential estimation methods 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method 13. Prony methods
- Literature
- IEEE Signal Processing Letters
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- IEEE Trans. on Signal Processing
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.
Information on course enrolment limitations: základy metod zpracování signálů a spektrální analýzy
Bi6446 Spectral Analysis of Time Series
Faculty of ScienceAutumn 2011
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc. - Prerequisites (in Czech)
- Bi5440 Signals & Linear Systems
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-BI)
- Mathematical Biology (programme PřF, N-EXB)
- Course objectives
- At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design of modified algorithms to process data of given particular characteristics
- Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ... 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences. 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation. 4. Spectral analysis algorithms for regularly and irregularly sampled signals. 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods. 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models. 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy. 8. Burg method. Unconstrained Least-Squares Method for AR model parameters. 9. Properties of methods for AR models, their comparison. Selection of AR-model order. 10. ARMA and MA models for power spectrum estimation 11. Sequential estimation methods 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method 13. Prony methods
- Literature
- IEEE Signal Processing Letters
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- IEEE Trans. on Signal Processing
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
Information on course enrolment limitations: základy metod zpracování signálů a spektrální analýzy
Bi6446 Spectral Analysis of Time Series
Faculty of ScienceAutumn 2010
- Extent and Intensity
- 3/0/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc. - Timetable
- Tue 13:00–15:50 Kontaktujte učitele
- Prerequisites (in Czech)
- Bi5440 Signals & Linear Systems
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-BI)
- Course objectives
- At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design of modified algorithms to process data of given particular characteristics
- Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ... 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences. 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation. 4. Spectral analysis algorithms for regularly and irregularly sampled signals. 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods. 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models. 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy. 8. Burg method. Unconstrained Least-Squares Method for AR model parameters. 9. Properties of methods for AR models, their comparison. Selection of AR-model order. 10. ARMA and MA models for power spectrum estimation 11. Sequential estimation methods 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method 13. Prony methods
- Literature
- IEEE Signal Processing Letters
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- IEEE Trans. on Signal Processing
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.
Information on course enrolment limitations: základy metod zpracování signálů a spektrální analýzy
Bi6446 Spectral Analysis of Time Series
Faculty of ScienceAutumn 2009
- Extent and Intensity
- 3/0/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc. - Timetable
- Mon 10:00–12:50 F01B1/709
- Prerequisites (in Czech)
- Bi5440 Signals & Linear Systems
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-BI)
- Course objectives
- At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design of modified algorithms to process data of given particular characteristics
- Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ... 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences. 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation. 4. Spectral analysis algorithms for regularly and irregularly sampled signals. 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods. 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models. 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy. 8. Burg method. Unconstrained Least-Squares Method for AR model parameters. 9. Properties of methods for AR models, their comparison. Selection of AR-model order. 10. ARMA and MA models for power spectrum estimation 11. Sequential estimation methods 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method 13. Prony methods
- Literature
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- IEEE Signal Processing Letters
- IEEE Trans. on Signal Processing
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
Information on course enrolment limitations: základy metod zpracování signálů a spektrální analýzy
Bi6446 Spectral Analysis of Biosignals
Faculty of ScienceAutumn 2008
- Extent and Intensity
- 3/0/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc. - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-BI)
- Course objectives
- The course will provide students with important facts on calculations of a frequency spectrum of typical biological time series. Principles of parametric and nonparametric algorithms for calculation of power spectral density estimations will be described in details. Properties and conditions of utilization of the particular methods will be discussed and their differences will be demonstrated in practical situations. After passing the course students are able to apply the described algorithms for a calculation of frequency spectra of data with given characteristics.
- Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ... 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences. 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation. 4. Spectral analysis algorithms for regularly and irregularly sampled signals. 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods. 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models. 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy. 8. Burg method. Unconstrained Least-Squares Method for AR model parameters. 9. Properties of methods for AR models, their comparison. Selection of AR-model order. 10. ARMA and MA models for power spectrum estimation 11. Sequential estimation methods 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method 13. Prony methods
- Literature
- IEEE Signal Processing Letters
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- IEEE Trans. on Signal Processing
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further Comments
- The course is taught annually.
Bi6446 Time Series Forecasting
Faculty of ScienceAutumn 2024
The course is not taught in Autumn 2024
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. Ing. Jiří Holčík, CSc.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Epidemiology and modeling (programme PřF, N-MBB)
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series prediction not only with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics - Learning outcomes
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics. - Syllabus
- 1. Why prediction usually fails.
- 2. Prediction – what is it?, preliminary analysis, transformation & adjustments, prediction models - method of simple forecasting.
- 3. Prediction models – regression, linear prediction (autoregressive models, moving average models).
- 4. Prediction models – linear prediction (exponential smoothing).
- 5. Judgemental forecasting.
- 6. Forecasting evaluation
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Time Series Forecasting
Faculty of ScienceSpring 2025
The course is not taught in Spring 2025
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. Ing. Jiří Holčík, CSc.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Epidemiology and modeling (programme PřF, N-MBB)
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series prediction not only with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics - Learning outcomes
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics. - Syllabus
- 1. Why prediction usually fails.
- 2. Prediction – what is it?, preliminary analysis, transformation & adjustments, prediction models - method of simple forecasting.
- 3. Prediction models – regression, linear prediction (autoregressive models, moving average models).
- 4. Prediction models – linear prediction (exponential smoothing).
- 5. Judgemental forecasting.
- 6. Forecasting evaluation
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Time Series Forecasting
Faculty of ScienceSpring 2024
The course is not taught in Spring 2024
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. Ing. Jiří Holčík, CSc.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Epidemiology and modeling (programme PřF, N-MBB)
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series prediction not only with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics - Learning outcomes
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics. - Syllabus
- 1. Why prediction usually fails.
- 2. Prediction – what is it?, preliminary analysis, transformation & adjustments, prediction models - method of simple forecasting.
- 3. Prediction models – regression, linear prediction (autoregressive models, moving average models).
- 4. Prediction models – linear prediction (exponential smoothing).
- 5. Judgemental forecasting.
- 6. Forecasting evaluation
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Time Series Forecasting
Faculty of ScienceAutumn 2023
The course is not taught in Autumn 2023
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. Ing. Jiří Holčík, CSc.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Epidemiology and modeling (programme PřF, N-MBB)
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series prediction not only with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics - Learning outcomes
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics. - Syllabus
- 1. Why prediction usually fails.
- 2. Prediction – what is it?, preliminary analysis, transformation & adjustments, prediction models - method of simple forecasting.
- 3. Prediction models – regression, linear prediction (autoregressive models, moving average models).
- 4. Prediction models – linear prediction (exponential smoothing).
- 5. Judgemental forecasting.
- 6. Forecasting evaluation
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Time Series Forecasting
Faculty of ScienceSpring 2023
The course is not taught in Spring 2023
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. Ing. Jiří Holčík, CSc.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Epidemiology and modeling (programme PřF, N-MBB)
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series prediction not only with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics - Learning outcomes
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics. - Syllabus
- 1. Why prediction usually fails.
- 2. Prediction – what is it?, preliminary analysis, transformation & adjustments, prediction models - method of simple forecasting.
- 3. Prediction models – regression, linear prediction (autoregressive models, moving average models).
- 4. Prediction models – linear prediction (exponential smoothing).
- 5. Judgemental forecasting.
- 6. Forecasting evaluation
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Time Series Forecasting
Faculty of ScienceAutumn 2022
The course is not taught in Autumn 2022
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. Ing. Jiří Holčík, CSc.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Epidemiology and modeling (programme PřF, N-MBB)
- Mathematical Biology (programme PřF, N-EXB)
- Modelling and Calculations (programme PřF, B-MA)
- Course objectives
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series prediction not only with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics - Learning outcomes
- At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics. - Syllabus
- 1. Why prediction usually fails.
- 2. Prediction – what is it?, preliminary analysis, transformation & adjustments, prediction models - method of simple forecasting.
- 3. Prediction models – regression, linear prediction (autoregressive models, moving average models).
- 4. Prediction models – linear prediction (exponential smoothing).
- 5. Judgemental forecasting.
- 6. Forecasting evaluation
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.
Bi6446 Spectral Analysis of Time Series
Faculty of ScienceAutumn 2011 - acreditation
The information about the term Autumn 2011 - acreditation is not made public
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
doc. Ing. Daniel Schwarz, Ph.D. (lecturer) - Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc. - Prerequisites (in Czech)
- Bi5440 Signals & Linear Systems
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-BI)
- Course objectives
- At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design of modified algorithms to process data of given particular characteristics
- Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ... 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences. 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation. 4. Spectral analysis algorithms for regularly and irregularly sampled signals. 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods. 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models. 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy. 8. Burg method. Unconstrained Least-Squares Method for AR model parameters. 9. Properties of methods for AR models, their comparison. Selection of AR-model order. 10. ARMA and MA models for power spectrum estimation 11. Sequential estimation methods 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method 13. Prony methods
- Literature
- IEEE Signal Processing Letters
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- IEEE Trans. on Signal Processing
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
Information on course enrolment limitations: základy metod zpracování signálů a spektrální analýzy
Bi6446 Spectral Analysis of Time Series
Faculty of ScienceAutumn 2010 - only for the accreditation
- Extent and Intensity
- 3/0/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Ing. Jiří Holčík, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc. - Prerequisites (in Czech)
- Bi5440 Signals & Linear Systems
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-BI)
- Course objectives
- At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design of modified algorithms to process data of given particular characteristics
- Syllabus
- 1. Basic terms, definitions – continuous and discrete signals, spectrum, energy, power, power spectral density, autocorrelation function, ... 2. Signals multiplication by windows and its influence to signal spectral characteristics. Estimates of autocorrelation function for complete and incomplete signal. Properties, consequences. 3. DFT – FFT, fast algorithms for a general number of samples. Properties, implementation. 4. Spectral analysis algorithms for regularly and irregularly sampled signals. 5. Nonparametric methods based on DFT algorithm – periodogram, Bartlett, Welch, and Blackman-Tukey methods. 6. Parametric methods for estimation of frequency spectrum – linear system model, AR, ARMA, and MA models. 7. Levinson-Durbin algorithm, properties, consequences of its application. Spectral estimation with maximum entropy. 8. Burg method. Unconstrained Least-Squares Method for AR model parameters. 9. Properties of methods for AR models, their comparison. Selection of AR-model order. 10. ARMA and MA models for power spectrum estimation 11. Sequential estimation methods 12. Eigenanalysis algorithms for spectrum estimation – Pisarenko harmonic decomposition method 13. Prony methods
- Literature
- IEEE Signal Processing Letters
- Handbook for Digital Signal Processing. (S.K.Mitra, J.F.Kaiser, eds.), New York, John Wiley & Sons 1993.
- Proakis, J.G. et al.: Advanced Digital Signal Processing. New York, Macmillan Publ. Comp. 1992.
- IEEE Trans. on Signal Processing
- Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. London, Prentice Hall 1975.
- Kay, S.M., Marple, S.L.: Spectrum Analysis - A Modern Perspective. Proc. IEEE, roč.69, č.11, Nov. 1981, s.1380-1418.
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- oral examination
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course is taught annually.
Information on course enrolment limitations: základy metod zpracování signálů a spektrální analýzy
- Enrolment Statistics (recent)