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Detecting Distributional Differences between Temporal Granularities for Exploratory Time Series Analysis

Author

Listed:
  • Sayani Gupta
  • Rob J Hyndman
  • Dianne Cook

Abstract

Cyclic temporal granularities are temporal deconstructions of a time period into units such as hour-of-theday and work-day/weekend. They can be useful for measuring repetitive patterns in large univariate time series data, and feed new approaches to exploring time series data. One use is to take pairs of granularities, and make plots of response values across the categories induced by the temporal deconstruction. However, when there are many granularities that can be constructed for a time period, there will also be too many possible displays to decide which might be the more interesting to display. This work proposes a new distance metric to screen and rank the possible granularities, and hence choose the most interesting ones to plot. The distance measure is computed for a single or pairs of cyclic granularities and can be compared across different cyclic granularities or on a collection of time series. The methods are implemented in the open-source R package hakear.

Suggested Citation

  • Sayani Gupta & Rob J Hyndman & Dianne Cook, 2021. "Detecting Distributional Differences between Temporal Granularities for Exploratory Time Series Analysis," Monash Econometrics and Business Statistics Working Papers 20/21, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2021-20
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp20-2021.pdf
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    References listed on IDEAS

    as
    1. Mahbubul Majumder & Heike Hofmann & Dianne Cook, 2013. "Validation of Visual Statistical Inference, Applied to Linear Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 942-956, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    data visualization; cyclic granularities; periodicities; permutation tests; distributional difference; Jensen-Shannon distances; smart meter data; R;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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