CIVIL-606 / 2 crédits

Enseignant(s): Skaloud Jan, Guerrier Stéphane

Langue: Anglais

Remark: Next time: Spring 2023 Block course


Frequency

Every 3 years

Summary

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.

Content

Keywords

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.

Resources

Bibliography

Applied Time Series Analysis with R: https://smac-group.github.io/ts/
An Introduction to Statistical Programming Methods with R: https://smac-group.github.io/ds/
Moodle: (TBD)

Notes/Handbook

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

https://gmwm.netlify.com

Websites

Moodle Link

Dans les plans d'études

  • Forme de l'examen: Exposé (session libre)
  • Matière examinée: Inference for large-scale time series with application to sensor fusion
  • Cours: 12 Heure(s)
  • Exercices: 8 Heure(s)
  • TP: 10 Heure(s)

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22