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Mining temporal data for information is often inhibited by a multitude offormats: irregular or multiple time intervals, point events that needaggregating, multiple observational units or repeated measurements on multipleindividuals, and heterogeneous data types. On the other hand, the softwaresupporting time series modeling and forecasting, makes strict assumptions onthe data to be provided, typically requiring a matrix of numeric data withimplicit time indexes. Going from raw data to model-ready data is painful. Thiswork presents a cohesive and conceptual framework for organizing andmanipulating temporal data, which in turn flows into visualization, modelingand forecasting routines. Tidy data principles are extended to temporal databy: (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 uniquelyidentify units over time. This tidy data representation most naturally supportsthinking of operations on the data as building blocks, forming part of a "datapipeline" in time-based contexts. A sound data pipeline facilitates a fluentworkflow for analyzing temporal data. The infrastructure of tidy temporal datahas been implemented in the R package "tsibble".

A new tidy data structure to support exploration and modeling of temporal data