CIVIL-606 / 2 credits

Teacher(s): Guerrier Stéphane, Skaloud Jan

Language: English

Remark: Next time: Spring 2025, block course


Every 3 years


Large-scale time series analysis is performed by a new statistical tool that is superior to other estimators of complex state-space models. The identified stochastic dependences can be used for sensor fusion by Bayesian (e.g. Kalman) filtering or for studying changes in natural/biological phenomena.



Statistics, modeling, estimation, sensor-fusion, time-series, Bayesian/Kalman filtering, state-space models

Learning Prerequisites

Required courses

Linear algebra, basic signal processing, basic statistics, basic programming

Learning Outcomes

By the end of the course, the student must be able to:

  • Calculate Allan/Wavelet variances from time time-series data
  • Identify structure of latent stochastic processes within a time series
  • Estimate model parameters together with its confidence intervals
  • Apply estimated models in state-space estimation

Expected student activities

The lectures alternates with labs during 2 week block. Students then work on a 32h project (distributed data or -after an agreement - their own data). The evaluation is based on written project report that is presented first orally before its due date - 1.5 month after block end.



Applied Time Series Analysis with R:
An Introduction to Statistical Programming Methods with R:
Moodle: (TBD)


Freely accessible website with “tutorial / exercises” and slides.


Moodle Link

In the programs

  • Exam form: Oral presentation (session free)
  • Subject examined: Inference for large-scale time series with application to sensor fusion
  • Lecture: 12 Hour(s)
  • Exercises: 8 Hour(s)
  • Practical work: 10 Hour(s)

Reference week

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