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A Method for Visualizing Multivariate Time Series Data

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  • Peng, Roger

Abstract

Visualization and exploratory analysis is an important part of any data analysis and is made more challenging when the data are voluminous and high-dimensional. One such example is environmental monitoring data, which are often collected over time and at multiple locations, resulting in a geographically indexed multivariate time series. Financial data, although not necessarily containing a geographic component, present another source of high-volume multivariate time series data. We present the mvtsplot function which provides a method for visualizing multivariate time series data. We outline the basic design concepts and provide some examples of its usage by applying it to a database of ambient air pollution measurements in the United States and to a hypothetical portfolio of stocks.

Suggested Citation

  • Peng, Roger, 2008. "A Method for Visualizing Multivariate Time Series Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(c01).
    • Handle: RePEc:jss:jstsof:v:025:c01
    DOI: http://hdl.handle.net/10.18637/jss.v025.c01
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    Cited by:

    1. Roberto Patuelli & Norbert Schanne & Daniel A. Griffith & Peter Nijkamp, 2012. "Persistence Of Regional Unemployment: Application Of A Spatial Filtering Approach To Local Labor Markets In Germany," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 300-323, May.
    2. Pierpaolo Pattitoni & Barbara Petracci & Massimo Spisni, 2011. "Fee Structure, Financing, and Investment Decisions: The Case of REITs," Working Paper series 30_11, Rimini Centre for Economic Analysis.
    3. Njenga, Carolyn Ndigwako & Sherris, Michael, 2020. "Modeling mortality with a Bayesian vector autoregression," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 40-57.
    4. Eva (E.F.) Janssens & Robin (R.) Lumsdaine & Sebastiaan (S.H.L.C.G.) Vermeulen, 2018. "An Epidemiological Model of Crisis Spread Across Sectors in The United States," Tinbergen Institute Discussion Papers 18-008/III, Tinbergen Institute.
    5. Moliner, Jesús & Epifanio, Irene, 2019. "Robust multivariate and functional archetypal analysis with application to financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 195-208.
    6. Carolyn Njenga & Michael Sherris, 2011. "Modeling Mortality with a Bayesian Vector Autoregression," Working Papers 201105, ARC Centre of Excellence in Population Ageing Research (CEPAR), Australian School of Business, University of New South Wales.
    7. Eva F. Janssens & Robin L. Lumsdaine & Sebastiaan H.L.C.G. Vermeulen, 2022. "An Epidemiological Model of Economic Crisis Spread across Sectors in the United States," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(4), pages 885-919, June.
    8. Xuanwu Yue & Jiaxin Bai & Qinhan Liu & Yiyang Tang & Abishek Puri & Ke Li & Huamin Qu, 2019. "sPortfolio: Stratified Visual Analysis of Stock Portfolios," Papers 1910.05536, arXiv.org.

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