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PRODID:-//University of Utah Math Department//High-dimensional Dynamic Factor Models for Non-stationary Time Series//EN
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X-WR-CALNAME:High-dimensional Dynamic Factor Models for Non-stationary Time Series
X-WR-CALDESC:High-dimensional Dynamic Factor Models for Non-stationary Time Series at University of Utah Mathematics Department
X-WR-TIMEZONE:America/Denver
BEGIN:VEVENT
UID:20160126T160000-giovanni-motta@math.utah.edu
DTSTART;TZID=America/Denver:20160126T160000
DTEND;TZID=America/Denver:20160126T170000
DTSTAMP:20260922T150858Z
SUMMARY:High-dimensional Dynamic Factor Models for Non-stationary Time Series
DESCRIPTION:Speaker: Giovanni Motta\, Columbia University\n\nHigh-dimensional time series are the most common type of dataset in the "big data" revolution. They arise in many areas, including neuroscience and econometrics. If the number of series is large, Principal Components Analysis is a powerful tool to reduce the dimensionality of the series. In the …

LOCATION:LCB 219

URL:https://www.math.utah.edu/research/colloquia/2016-01-26-giovanni-motta/
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