tsibble: Tidy data structures to support exploration and modeling of temporal-context data

The conventional matrix structure that underlies time series models in R does not easily accommodate a few complications, such as multiple variables, heterogeneous data types, low time resolutions, implicit missing values, and multilevel. This work addresses the broader issues of better data structures and modern data pipelines for analysing and visualising temporal-context data. We extend the tidy data concept to temporal data, and note that the “molten” data structure is flexible enough to handle heterogeneity, low time resolutions, and implicit missing values. There are two constraints required to turn the “molten” data into a valid temporal data: (1) an explicitly declared index variable containing timestamps; (2) a constraint uniquely identifies the multiple units of measurements, which is referred to as a “key”. A syntactical approach is introduced to describe nested or crossed data structure, which employs the “key”. Based on the tidy temporal data, a data pipeline is discussed and formulated to facilitate time-based transformation and visualisation. A case study is included to demonstrate the tidy structure and the data pipeline ideas and usage.