Sequential detection of structural changes in irregularly observed data

Date

Jul 19, 2018

Time

2:40 PM - 3:40 PM

Speaker

Tobias Kley

Affiliation

Humboldt-Universität Berlin

Series

TUD Mathematik AG Analysis & Stochastik

Language

en

Main Topic

Mathematik

Other Topics

Mathematik

Host

Prof. Dr. R. Schilling

Description

Online surveillance of time series is traditionally done with the aim to identify changes in the marginal distribution under the assumption that the data between change-points is stationary and that new data is observed at constant frequency. In many situations of interest to data analysts, the classical approach can be too restrictive to be used unmodified. We propose a unified system for the monitoring of structural changes in streams of data where we use generalised likelihood ratio-type statistics in the sequential testing problem, obtaining the flexibility to account for the various types of changes that are practically relevant (such as, for example, changes in the trend of the mean). Our method is applicable to sequences where new observations are allowed to arrive irregularly. Early identification of changes in the trend of financial data can assist to make trading more profitably. In an empirical illustration we apply the procedure to intra-day prices of components of the NASDAQ-100 stock market index. This is joint work with Piotr Fryzlewicz (London School of Economics).

Links

• AG Analysis & Stochastik

Last modified: Jul 9, 2018, 12:46:23 PM