Thursday
No. 1
08:00-12:00 |
Teaching
Lecture:
- Introduction
- Multivariate random variables (Chap. 2)
Exercises: 2.1, 2.2, 2.3
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Thursday
No. 2
08:00-12:00 |
Teaching
Regression based methods, 1st part:
- Introduction (Sec. 3.1)
- The General Linear Model, including OLS-, WLS-, and ML-estimates
(Sec. 3.2)
- Prediction in the General Linear Model (Sec. 3.3)
Exercises: 3.1, 3.4
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Thursday
No. 3.
08:00-12:00 |
Teaching
Regression based methods, 2nd part:
- Trend models, including updates of estimates and adaptive LS (Sec.
3.4 [except p 57(mid)-59], Sec. 3.6)
Cursory material: Sec. 3.5
Exercise: Computer Exercise (Assignment No. 1)
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Thursday
No. 4.
08:00-12:00 |
Teaching
Stochastic processes:
- Stochastic processes in general: Sec 5.1, 5.2, 5.3 [except 5.3.2]
- Shift operators: Sec 4.5 (for understanding 5.3)
- If time permits: MA, AR, and ARMA-processes, Sec. 5.5 (disregard
'spectra' like (5.67), (5.72), (5.85), (5.86), (5.112))
Cursory material: Sec. 5.3.2
Exercise: 5.1, 5.7, 5.4
Comment: To get started more quickly with the traditional time series
analysis we will come back to chapter 4 and the fequency descrioptions
at a later stage. For this reason, as for now, you will have to
disregard spectra / fequency in e.g. Sec. 4.5.
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Thursday
No. 5.
08:00-12:00 |
Teaching
Stochastic processes, 2nd part:
- Non-stationary models, Sec. 5.6
- Optimal Prediction, Sec. 5.7
Identification of univariate time series models, 1st part:
- Introduction, Sec. 6.1
- Estimation of auto-covariance and -correlation, Sec. 6.2.1 (and the
intro. to 6.2)
Exercise: Computer Exercise (Assignment No. 2). |
Thursday
No. 6.
08:00-12:00 |
Teaching
Identification of univariate time series models, 2nd part:
- Using SACF, SPACF, and SIACF for suggesting model structure, Sec. 6.3
- Estimation of model parameters, Sec. 6.4
Except:
The extended linear model class in Sec. 6.4.2 (page 162)
Sec 6.4.3.2
The extended model class in sec. 6.4.4.1
Cursory material:
- Sec 6.4.3.2
- Extended model classes as mentioned above (we will come back to this
in Chapter 8)
Exercise: Computer Exercise (carry on with Assignment No. 2)
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Thursday
No. 7.
08:00-12:00 |
Teaching
Identification of univariate time series models, 3rd part:
-Estimation of model parameters, Sec. 6.4 (with exceptions as for
lecture 6)
-Model order, Sec. 6.5
-Model validation, Sec. 6.6
-Example, Sec. 6.7: Read on your own - instead we will consider the CO2
time series from lecture 6
Cursory material:
-Spectral analysis, Sec. 5.4 and 7.1
Exercises: 6.1, 6.6, 6.8
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Thursday
No. 8.
08:00-12:00 |
Teaching
Input-Output systems, 1st part:
- Linear systems, Chap. 4, except Sec. 4.5-4.7 (4.5 were used in
lecture 4)
- Cross Correlation Functions, Sec. 6.2.2 (reread the theory on CCF on
pages
103-104 (eq. 5.23 - 5.27) and 105-106 (from theorem 5.4)
Cursory material:
- Cross spectrum, Sec. 7.3-7.4
Exercise: Assignment No. 3 (Build and use an ARMA model) |
Thursday
No. 9.
08:00-12:00 |
Teaching
Input-Output systems, 2nd part:
- The z-transform, Sec. 4.4
- Transfer function models; identification, estimation, validation,
prediction, Chap. 8 (except Sec. 8.1.2, Theorem 8.4)
- Estimation in extended model classes, Sec. 6.4.4.1
Cursory material:
- Spectral relations, Sec. 8.1.2
- Theorem 8.4
Exercises: first will be in CampusNet file sharing and 8.2
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Thursday
No. 10
08:00-12:00 |
Teaching
Multivariate time series
Chap. 9
Exercise: Assignment No. 4 (Modelling Heat Consumption)
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Thursday
No. 11.
08:00-12:00 |
Teaching
State space models, 1st part:
Model: Sec. 10.1
The Kalman filter: Sec. 10.3 (except 10.3.2)
Cursory material: 10.3.2
Exercise: Carry on with assignment 4
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Thursday
No. 12.
08:00-12:00 |
Teaching
State space models, 2nd part:
ARMA-models on state space form, Sec. 10.4 (not 10.4.1)
ML-estimates of state space models, Sec. 10.6
Exercise: Assignment No. 5
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Thurday
No. 13.
08:00-12:00 |
Teaching
Recursive estimation:
Introduction to Chap.11
Recursive LS, Sec. 11.1
Cursory material:
Model based adaptive estimation, Sec. 11.4 (Kalman filter for linear
models)
Some ideas for Bachelor or Master projects
Course evaluation
Exercise: Carry on with assignment No. 5
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