732A80 · 6 credits · Advanced level (A1F) · Statistik
Time Series and Sequence Learning
Active · next start HT 2026
63%
passed the latest regular sitting
DAT1 · 27 Oct 2025 · 19 students
80%
passed the regular sittings, all sittings over the last 5 years
DAT1 · 114 students
Unusually hard in 2025/26
63% passed the ordinary exam in 2025/26 (19 students), against 82% on average in earlier years (95 students).
Other module
DAT1 Examination
- A
- B
- C
- D
- E
- F
Main rounds
80% passed · 114 students
- 63%19
- 72%18
- 97%30
- 91%22
- 68%25
Later rounds
73% passed · 44 students
- 100%7
- 38%8
- 67%6
- 83%6
- 83%12
- 60%5
- Pass a later round: 73% (44 students), compared with 80% (114 students) on the ordinary exam
- Get B or A: 27% (158 students)
Show small sittings (3)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 75%4
- 100%1
- 100%4
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB1 Laboratory work
- P
Main rounds
100% passed · 107 students
- 100%25
- 100%16
- 100%38
- 100%28
Later rounds
100% passed · 24 students
- 100%5
- 100%10
- 100%9
- Pass a later round: 100% (24 students), compared with 100% (107 students) on the ordinary exam
Show small sittings (6)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%2
- 100%3
- 100%2
- 100%3
- 100%1
- 100%1
About the course
Aim
After completion of the course, the student shall on an advanced level be able to:
- apply methods for the analysis of sequential data
- account for principles for sample selection, estimation and validation ofsequential models
- use statistical and numerical software to fit time series modelsanalyze inference about time series components, and compute forecasts andtheir statistical uncertainty
- evaluate the generalization capacity of the statistical relationships to make forecasts
Content
The course provides knowledge about state-of-the-art methods needed forprofessional work in which sequential data are explored, modified, modelled and assessed. The course comprises:
- Linear autoregressive models
- Nonlinear autoregressive model, including temporal convolutional networks
- State space models, Kalman filtering and smoothing
- Nonlinear state space models and Sequential Monte Carlo filtering
- Recurrent neural networks
- Model estimation, validation, and forecasting
- Examiner
- Zheng Zhao
- Main field of study
- Statistik