  Mining temporal data for information is often inhibited by a multitude of
formats: irregular or multiple time intervals, point events that need
aggregating, multiple observational units or repeated measurements on multiple
individuals, and heterogeneous data types. On the other hand, the software
supporting time series modeling and forecasting, makes strict assumptions on
the data to be provided, typically requiring a matrix of numeric data with
implicit time indexes. Going from raw data to model-ready data is painful. 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 "tsibble".
