Lecture
Vector Autoregression: Modeling Vector-Valued Time Series
In course
DEMO: commodo nostrud ex sit
Ea fugiat commodo do ullamco dolor esse enim consectetur. Cillum ut commodo velit velit qui occaecat id. Culpa minim mollit deserunt do minim amet minim tempor minim qui proident do esse veniam. Do sunt reprehenderit occaecat adipisicing elit. Do officia proident occaecat dolor esse laborum proident. Irure occaecat dolor aliqua aute aute amet est ipsum nisi.
Description
This lecture by the instructor covers the concept of Vector Autoregression (VAR) for modeling vector-valued time series. Starting from the basics of AR processes, the lecture explains how to extend them to vector-valued processes using the VAR(p) model. The stability of VAR(p) models is discussed, along with examples and calculations of reverse characteristic polynomials. The lecture also delves into the computation of auto-covariance matrices, Yule-Walker equations, and autocorrelations for VAR processes. The presentation concludes with a summary of the key topics covered, including linear processes, spectral decomposition, estimation methods, forecasting, and multivariate models.
Instructor
exercitation dolore sunt officia
Eiusmod officia aliquip ipsum id. Aliqua velit labore ullamco magna velit enim sit ipsum minim veniam. Qui Lorem velit veniam occaecat aliquip et non elit aliquip. In dolore fugiat officia nulla eu enim ea eiusmod dolor excepteur quis. Nisi proident tempor qui consequat tempor ut ea labore eiusmod irure.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (34)
Vector Autoregression (VAR): Sampling Properties and Examples
Covers Vector Autoregression (VAR) in time series analysis, including sampling properties and examples of VAR processes.
Vector Autoregression
Explores Vector Autoregression for modeling vector-valued time series, covering stability, Yule-Walker equations, and spectral representation.
Time Series Models: Autoregressive Processes
Explores time series models, emphasizing autoregressive processes, including white noise, AR(1), and MA(1), among others.
Parametric Signal Models: Matlab Practice
Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.
Forecasting & Long Memory: Time Series
Explores forecasting methods and long memory in time series analysis.