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Activity Number: 70 - Nonlinearites and Information
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #329375
Title: A Spectral-Based Kolmogorov-Smirnov Method for Detecting the Information Loss of Temporal Aggregation
Author(s): Bu Hyoung Lee*
Companies: Loyola University Maryland
Keywords: temporal aggregation; information loss; spectral analysis; Kolmogorov-Smirnov test; time series

In this article, we develop a spectral method for identifying information loss on the process characteristics of an aggregate series. Even though temporal aggregation is a simple and efficient technique summarizing sequential observations, it causes substantial structural changes in a process because a non-aggregate series of a relatively high frequency is transformed into an aggregate series of a relatively low frequency. The effects of temporal aggregation can be explained with changes in the spectral density function. Then, we propose a spectral-based Kolmogorov-Smirnov test for detecting an aggregation resulting significant structural changes to white noise.

Authors who are presenting talks have a * after their name.

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