Some Methods for Analyzing Big Dependent Data

Bibliographic Data

ID19418790
AuthorsRuey S Tsay (0000-0002-4949-4035, The University of Chicago Booth School of Business, Chicago, IL 60637 (), corresponding author)
Year2016
Volume34
Issue4
Pages673-688
Publication date2016-10-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2016.1148040
OpenAlexW2502584369
LanguageEN
Citations received2
References cited18

We consider an approach to analyze big data of time series. Big dependent data are first transformed into functional time series of densities via nonparametric density estimation. We then discuss some tools for exploratory data analysis of the resulting functional time series. The tools employed include K-means cluster analysis and tree-based classification. For modeling, we propose a threshold approximate-factor model and a Hellinger distance autoregressive model for functional time series of continuous densities. The latent factors of factor models are estimated by functional principal component analysis. Cross-validation and Hellinger distance are used to select the number of principal component functions. For prediction of high-dimensional time series, we use the results of cluster analysis to obtain parsimonious models. We demonstrate the proposed analysis by considering the demand of electricity, the behavior of daily U.S. stock returns, and U.S. income distributions

Autoregressive model · Data mining · Econometrics · Factor analysis · Functional principal component analysis · Hellinger distance · Machine learning · Nonparametric statistics · Principal component analysis · Statistics · Time series · Complex Systems and Time Series Analysis · Computer Science · Financial Risk and Volatility Modeling · Mathematics · Time Series Analysis and Forecasting · Artificial Intelligence

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