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Wananga landing
Wananga landing
Topic

Parsimonious models for multivariate time series

15 August 2023

Host Faculty: Engineering

General Subject Area: Statistics and Data Science

Project Level: PhD

HOW TO APPLY

The established framework for modelling multivariate time series is the vector autoregression moving average (VARMA) models. This class of models requires a large set of parameters. We are interested in either sparse structures using statistical learning approaches such as graphical lasso or more parsimonious models such as the recently proposed ZAR models. We are interested in models for both analysis and forecasting. This is a broad area of research: the specific project will depend on the student's interests.

 
Supervisors

Supervisor: Marco Reale

 
Does the project come with funding

No

 
Final date for receiving applications

Ongoing

 
Keywords

Time series analysis; statistical learning; graphical modelling

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