STOR Colloquium: Tucker McElroy, US Census Bureau
U.S. Census Bureau
Casting Vector Time Series: Forecasting, Imputation, and Signal Extraction In the Context of Big Data
Recursive algorithms, based upon the nested structure of Toeplitz covariance matrices arising from stationary processes, are presented for the efficient computation of multi-step ahead forecast error covariances for nonstationary vector time series. Further, we discuss time reversal to forecast the past, and algorithms for imputation of missing values. These quantities are required to quantify multi-step ahead forecast error and signal extraction error. The methods are applied to daily retail data exhibiting trend dynamics and seasonality.
Short bio: Dr. McElroy is Senior Time Series Mathematical Statistician at the U.S. Census Bureau, where he has served the last 15 years as a researcher and consultant on seasonal adjustment problems. He has a B.A. from Columbia University (1996), and a Ph.D. in mathematics from University of California, San Diego (2001).