About this course

The course provides a theoretical and practical introduction to handling, processing, and analyzing data with dependence along one axis, such as sequential or time series data. The course focuses on applications from biology, industrial applications, and finance. The following topics will be covered:

  • preprocessing of time series data
  • stochastic processes and properties
  • forecasting of time series data
  • anomaly/outlier detection in time series data
  • classification/clustering in time series data

The course presents statistical and machine learning approaches. Students will learn to build effective and accurate models that, depending on the application, can contribute to several of UN's sustainability goals, among others 3, 11, 12, 14, 15.

Learning outcome

Insight into relevant problems and models to analyze sequential and time series data from statistics and machine learning perspectives. Basic understanding of properties and definitions related to time series analysis. Practical hands-on experience in preprocessing, analyzing, and interpreting results for real-world datasets.

  • Learning activities

    • Lectures
    • Assignments with presentations (paper and pencil, programming)

  • Teaching support

    • Supplementary online material
    • Q&A sessions with teaching assistants

  • Syllabus

    Announced in the lecture

  • Prerequisites

    • Machine learning / statistics (DAT200 / STAT200)
    • Introductory programming course (INF120 or similar)
    • Basic calculus and linear algebra (MATH113 / 131 or MATH122 + 123 or similar)

  • Recommended prerequisites

    R programming (will be covered in the lecture, but basic knowledge is an advantage)

  • Assessment method

    Combined evaluation (A-F):

    • Portfolio evaluation of exercises including presentation during the term count 40% of the final grade. Any sessions with compulsory attendance will be announced at the start of the course.
    • Final written exam (3.5 hours) counts 60% of the final grade.


    Portfolio Karakterregel: Letter grades School exam Karakterregel: Letter grades Hjelpemiddel: B1 Calculator handed out, no other aids

  • About use of AI

    Exam: K1 - no use of AI

    Compulsory activity: K3 - Full use of AI. Use of AI is permitted, but must be in line with the guidelines for the use of artificial intelligence (AI) at NMBU.

    AI is not used directly in the lessons but is a natural support for programming and rehearsal of the curriculum.

    Descriptions of AI-category codes.

  • Examiner scheme

    An external censor will participate together with the internal censor in designing the portfolio evaluation and the written exam and including evaluation guidelines. The external censor checks the internal censor's assessment of the final exams of a random selection of candidates as a calibration at certain intervals in line with the faculty's guidelines for censoring.

  • Teaching hours

    • Lectures and presentation: 4h per week
    • Exercises: 2h per week

  • Admission requirements

    Science