TMA4285 - Time Series
12.08.19: Welcome to the course! The first lecture will take place in B2 Tuesday August 20. The lectures will be given in English if any non-Scandinavian speaking students attend the lectures.
20.09.2019: Next week we start on the statistics part of the course, and will talk about:
Brockwell and Davis: Introduction to Time Series and Forecasting, Springer
For some concepts and proofs if you happen to skip some lectures:
Brockwell and Davis: Time Series: Theory and Methods, Springer 1991 (2.edition)
For R examples needed for mandatory excercises:
Shumway and Stoffer: Time Series Analysis and Its Applications (With R Examples), Springer 2017.
Venables, Ripley (2002). Modern applied statistics with S (4th ed.). New York: Springer. Chapter 14: Time Series Analysis
GUM (2008): Bureau International des Poids et Mesures, Guide to the Expression of Uncertainty in Measurement
The ISI glossary of statistical terms in a number of languages
Box, Jenkins, Reinsel (2008): Time Series Analysis (Forecasting and Control) (4th ed.), Wiley
Recommended preliminary courses:
A more advanced related course:
Karolina Agneta Brodin (INDØK), Gabriel Kordon (Exchange), Erik Dengerud (INDMAT)
24.09.19: Meeting 1
15.10.19: Meeting 2
xx.11.19: Meeting 3 (Final meeting)
Tuesday 10:15-12:00 B2 Berg
Thursday 12:15-14:00 VA2 Varmeteknisk
Thursday 14:15-15:00 Nullrommet (room number 380A), 3rd floor in Central building 2, nordre lavblokk.
Tuesday 13:15-14:00 Room 1138, Sentralbygg 2, Gløshaugen
Lecture Plan: Brockwell and Davis 2016
Week 34: Stochastic processes, Chap 1 and 2. App A, B, C
Week 35: Stationary processes, Chap 2.
Week 36: Stationary processes, Chap 2.
Week 37: No lectures. Work on Exercise 3
Week 38: ARMA models, Chap 3
Week 39: Statistics for ARMA models, Chap 5
Week 40: Statistics for ARMA models, Chap 5
Week 41: Nonstationary and seasonal time series, Chap 6.1-5
Week 42: Ex3 and Ex8 discussion, Chap 9 State-Space Models, Chap 9.1-5
Week 43: No lectures. Work on Excercise 8
Week 44: Time Series Models for Financial Data, Chap 7.1 Historical Overview, 7.2 GARCH Models
Week 47: Main ideas in the course. Repetition. Exam questions.
Ex 1, Week 35: GT: 1, BD: App A: 4,5,6, Chap 1: 2-5,8 Chap 2: 1. LF
Ex 2, Week 36: GT: 2, BD: Chap 2: 5, 7-9, 13, 15, 18-20. LF
Ex 3, Week 37: Exercise 3, DATA Mandatory! The deadline for submission is Sunday September 22th (23:59). Send the report by email to Jorge. Remember to include the names of all group members.
Ex 4, Week 39: GT: 3, BD: Chap 3: 1b, 4, 6, 7, 8, 12. LF
Ex 5, Week 40: GT: 4, BD: App A: 7, Chap 5: 1,3,4,8,13 LF
Ex 6, Week 41: GT: 5, BD: Chap 5: 9, 10, 11, 12.LF Solution to exercise 5.11 in Ex 5
Ex 7, Week 42: GT: 6, BD: Chap 6:1, 2, 5, 6, 11.LF
Ex 8, Week 43: Exercise 8, DATA, Mandatory! The deadline for submission is Sunday November 3th (23:59). Send the report by email to Jorge. Remember to include the names of all group members.
Ex 9, Week 45: Chap 9: 1, 3, 9, 11, 12, 17.LF
Ex 10, Week 46: Chap 7: 1, 2, 3; LF
Week 47: Previous exams and exercises
Written, 27.11.2019, 4 hours.
Permitted examination support material: C:
– Tabeller og formler i statistikk, Tapir forlag,
– K.Rottman. Matematisk formelsamling,
– Stamped yellow A5 sheet with your own handwritten notes,
– Determined, single calculator
As for previous exams you are allowed to bring with you one yellow A5 sheet with your own formulas and notes. The sheet of paper need to be stamped and you can get it at the Department office, seventh floor SII.
Work (20/100): Two exercises are mandatory and should be handed in to Jorge and count 10% each on the final grade. You can work in groups of 1-3. Use the name of all group members when you hand in the exercises, not student numbers or candidate numbers. The submission deadline and more information will be announced later.
2) Lectures (in particular definition and use of conditional expectation and Hilbert space theory - which is not in the book)
3) Chapters from Brockwell and Davis 2016:
1. Introduction 2. Stationary processes 3. ARMA models 4. Spectral analysis (out) 5. Modeling and forecasting with ARMA processes 6. Nonstationary and seasonal time series models (6.1-5) 7. Time Series Models for Financial Data (7.1-2) 8. Multivariate time series (out) 9. State space models (9.1-9.5) 10. Forecasting techniques (out) Appendix A, B, C