A New Tidy Data Structure to Support Exploration and Modeling of Temporal Data. Issue 3 (2nd July 2020)

• Record Type: Journal Article
• Authors: Wang, Earo; Cook, Dianne; Hyndman, Rob J.
• Is Part Of: Journal of computational and graphical statistics. Volume 29:Issue 3(2020)
• Page Start: 466
• Page End: 478
• Publication Date: 2020-07-02
• Subjects: Data pipelines -- Data science -- Data wrangling -- Forecasting -- Longitudinal data -- Time series; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2019.1695624
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract: Mining temporal data for information is often inhibited by a multitude of formats: regular or irregular time intervals, point events that need aggregating, multiple observational units or repeated measurements on multiple individuals, and heterogeneous data types. This work presents a cohesive and conceptual framework for organizing and manipulating temporal data, which in turn flows into visualization, modeling, and forecasting routines. Tidy data principles are extended to temporal data by: (1) mapping the semantics of a dataset into its physical layout; (2) including an explicitly declared "index" variable representing time; (3) incorporating a "key" comprising single or multiple variables to uniquely identify units over time. This tidy data representation most naturally supports thinking of operations on the data as building blocks, forming part of a "data pipeline" in time-based contexts. A sound data pipeline facilitates a fluent workflow for analyzing temporal data. The infrastructure of tidy temporal data has been implemented in the R package, called tsibble . Supplementary materials for this article are available online.