The Effects of Temporal Aggregation on Search Engine Data

Heather L.R. Tierney, Kim, Jiyoon (June) and Zafar Nazarov

MPRA Paper from University Library of Munich, Germany

Abstract: Using structured machine learning, this paper examines the effect that temporal aggregation has on big data from Google Analytics and Google Trends. Specifically, daily and weekly data from the Charleston Area Convention and Visitors Bureau (CACVB) website from January 2008 to March 2009 via Google Analytics and weekly, monthly, and quarterly data from Google Trends for seven economic variables from 2004 to 2011 are examined. Taking into account the different levels of aggregation, the CDFs and the estimated regression results are examined. The Kolmogorov-Smirnov test rejects the null of equivalent data distributions in the vast majority of cases for the CACVB data, but this is not the case for the economic variable. Through data mining, this paper also finds that aggregation has the potential of affecting the level of integration and the regression results for both the CACVB data and the seven economic variables.

Keywords: Big Data; Machine Learning; Data Mining; Aggregation; Unit roots; Scaling Effects; Normalization Effects (search for similar items in EconPapers)
JEL-codes: C19 C43 C55 (search for similar items in EconPapers)
Date: 2018-01-30
New Economics Papers: this item is included in nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:84474

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