Time Series and Sequence Learning, 6 credits

Main field of study

Course level

Advancement level

Course offered for

• Master's Programme in Statistics and Machine Learning

Entry requirements

• Bachelor's degree equivalent to a Swedish Kandidatexamen of 180 ECTS credits in one of the following subjects:
  • statistics
  • mathematics
  • applied mathematics
  • computer science
  • engineering
• Completed courses in
  • calculus
  • linear algebra
  • statistics
  • machine learning
  • programming
• English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
  Exemption from Swedish
• At least 30 ECTS credits passed from semester 1 and 2 Master's Programme in Statistics and Machine Learning, including the course Machine Learning 9 ECTS credits, or  the equivalent

Intended learning outcomes

After completion of the course, the student should on an advanced level be able to:
- apply methods for the analysis of sequential data
- account for principles for sample selection, estimation and validation of
sequential models
- use statistical and numerical software to fit time series models
- analyze inference about time series components, and compute forecasts and
their statistical uncertainty
- evaluate the generalization capacity of the statistical relationships to make forecasts

Course content

The course provides knowledge about state-of-the-art methods needed for
professional 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

Teaching and working methods

The teaching comprises lectures, exercise sessions, and computer laboratory work.
Beyond this, the student must practice self-study. Language of instruction: English.

Examination

The course is examined by:

• written reports in groups of laboratory tasks, grade scale: EC, P/F
• individual written computer examination, grade scale: EC

For Pass (E) as the final grade, at least E is required on the individual written computer examination and Pass on other parts. Higher grades are based on the individual written computer examination.

Detailed information can be found in the study instructions.

If special circumstances prevail, and if it is possible with consideration of the nature of the compulsory component, the examiner may decide to replace the compulsory component with another equivalent component.

If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it.

If the coordinator has recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.

An examiner may also decide that an adapted examination or alternative form of examination if the examiner assessed that special circumstances prevail, and the examiner assesses that it is possible while maintaining the objectives of the course.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.

Grades

Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is conducted in such a way that there are equal opportunities with regard to sex, transgender identity or expression, ethnicity, religion or other belief, disability, sexual orientation and age.

If special circumstances prevail, the vice-chancellor may in a special decision specify the preconditions for temporary deviations from this course syllabus, and delegate the right to take such decisions.

Department