TMA4285 Time Series Models

(Fall 2006)

Textbook:   Peter J. Brockwell, Richard A. Davis. Introduction to Time Series and Forecasting, 2002, Springer Verlag.


Pensum:
Chapter 1
Chapter 2 (except 2.5.1, 2.6)
Chapter 3
Chapter 4
Chapter 5 (except 5.1.2, 5.1.3)
Chapter 6 (except 6.3, 6.6)


Exam (solutions)


Exam 2005


Schedule:
Lectures: Monday 12:15-14:00, room F1
Thursday (group 1) 14:15-16:00, room EL2
Friday (group 2) 14:15-16:00, room S1

Exercises: Thursday (group 1) 16:15-17:00, room EL2
Friday (group 2) 16:15-17:00, room S1


Messages:

13.10.06: Results of the midterm exam and solutions are below.

2.10.06: Midterm exam is 9.10.06 (Monday), 12:15-14:00, room F1. Pensum: Chapter 1 (whole), Chapter 2 (without 2.5.1, 2.5.2, 2.6), Chapter 3 (3.1, 3.2). Tillatte hjelpemidler: alle trykte og håndskrevne hjelpemidler tillatt. Alle kalkulatorer tillatt. (By the way, calculator is necessary).

24.08.06: I have received the following information: "Mandager 14-15 kan du ikke legge undervisning. Da er det tillitsrepresentant time og skal være undervisningsfri for alle studenter." Therefore I transfer øvinger from Monday 14:15-15:00 to Thursday 16:15-17:00. Please let me know if this is not convenient.

23.08.06: I have received a message from one participant of our course. He asks for a change of the schedule, namely, to transfer our Friday lecture to Thursday (the same time 14:15 - 16:00). Is this OK for others? Please let me know (as soon as possible) if Thursday, 14:15 - 16:00, is not convenient. If I receive no messages I transfer the Friday lecture to Thursday. In this case the first lecture will be Monday 28 August.

17.08.06: Something is wrong with the schedule (collision with other courses) so we have to change the schedule. We meet August 22, 10:15 but in room F6 and define the new schedule. I would appreciate if each of you, who intends to take TMA4285, send me e-mail and indicate days/time which are not convenient.

15.08.06: First meeting is August 22, 10:15, room F3.


Midterm exam (solutions)

Midterm exam (results)

Plan:
Lecture 1.
(28.08.06)
Introduction. Repetition from Probability theory.
Appendix A
Homework: -
Lecture 2.
(31.08.06/
1.09.06)
Repetition (continued). Time series models.
Appendix A, Chapter 1 (1.1, 1.2, 1.3, 1.4).
Homework: A3, 1.1, 1.3, 1.4, 1.5, 1.6
Lecture 3.
(4.09.06)
AR(1) process. Sample ACVF and ACF. Estimation of trend and seasonal components.
Chapter 1 (1.4, 1.5).
Homework: 1.7, 1.8, 1.11
Lecture 4.
(7.09.06/
8.09.06)
Estimation of trend and seasonal components. Testing the noise.
Chapter 1 (1.5, 1.6), Appendix B.
Homework: 1.10, 1.13, 1.16
Lecture 5.
(11.09.06)
Basic properties of stationary processes. Linear process.
Chapter 2 (2.1, 2.2), Appendix C.
Homework: 2.1, 2.2, 2.3
Lecture 6.
(14.09.06/
15.09.06)
Linear process (continued).
Chapter 2 (2.2, 2.3).
Homework: 2.4, 2.5
Lecture 7.
(18.09.06)
ARMA(1,1). Stationary processes (estimation, prediction).
Chapter 2 (2.3, 2.4, 2.5).
Homework: 2.8, 2.9a, 2.10
Lecture 8.
(21.09.06/
22.09.06)
Stationary processes (prediction).
Chapter 2 (2.5).
Homework: 2.11, 2.12, 2.14
Lecture 9.
(25.09.06)
Prediction operator. ARMA models.
Chapter 2 (2.5), Chapter 3 (3.1, 3.2).
Homework: 2.13, 2.15
Lecture 10.
(28.09.06/
29.09.06)
ARMA(p,q). ACF and PACF.
Chapter 3 (3.1, 3.2).
Homework: -
Lecture 11.
(2.10.06)
ARMA models (ACF, PACF, Prediction).
Chapter 3 (3.2, 3.3).
Homework: 3.1, 3.2, 3.3 (something)
Lecture 12.
(5.10.06/
6.10.06)
ARMA models. Estimation in ARMA models.
Chapter 3 (3.3), Chapter 5 (5.1).
Homework: 3.4, 3.6
Midterm exam.
(9.10.06)



Lecture 13.
(12.10.06/
13.10.06)
Estimation in ARMA models.
Chapter 2 (2.5.2), Chapter 5 (5.1).
Homework: 2.18, 2.20
Lecture 14.
(16.10.06)
Innovations algorithm.
Chapter 2 (2.5.2), Chapter 3 (3.3).
Homework: 3.7
Lecture 15.
(19.10.06/
20.10.06)
Prediction and estimation in ARMA models.
Chapter 3 (3.3), Chapter 5 (5.1).
Homework: 5.1
Lecture 16.
(23.10.06)
Least squares and maximum likelihood methods in ARMA model estimation.
Chapter 5 (5.1, 5.2).
Homework: 5.3
Lecture 17.
(26.10.06/
27.10.06)
Order selection for ARMA models.
Chapter 5 (5.5).
Homework: 5.8
Lecture 18.
(30.10.06)
The AICC criterion. Diagnostic checking and forecasting with ARMA processes.
Chapter 5 (5.5.2, 5.3, 5.4).
Homework: 3.5
Lecture 19.
(2.11.06/
3.11.06)
Forecasting with ARMA processes. ARIMA models.
Chapter 5 (5.4), Chapter 3 (3.3), Chapter 6 (6.1, 6.2, 6.3).
Homework: 3.9
Lecture 20.
(6.11.06)
Forecasting ARIMA processes.
Chapter 6 (6.4).
Homework: 6.5
Lecture 21.
(9.11.06/
10.11.06)
Seasonal ARIMA models. Spectral densities.
Chapter 6 (6.5), Chapter 4 (4.1).
Homework: 6.1
Lecture 22.
(13.11.06)
Spectral densities.
Chapter 4 (4.1).
Homework: 4.4
Lecture 23.
(16.11.06/
17.11.06)
Spectral analysis.
Chapter 4 (4.1, 4.2, 4.3, 4.4).
Homework: -
Lecture 24.
(20.11.06)
Repetition.