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- W2912706434 abstract "Mining temporal 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 types. On the other hand, the software supporting time series modeling and forecasting, makes strict assumptions on the to be provided, typically requiring a matrix of numeric with implicit time indexes. Going from raw to model-ready 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 principles are extended to temporal 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 representation most naturally supports thinking of operations on the as building blocks, forming part of a data in time-based contexts. A sound pipeline facilitates a fluent workflow for analyzing temporal data. The infrastructure of tidy temporal has been implemented in the R package tsibble." @default.
- W2912706434 created "2019-02-21" @default.
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- W2912706434 date "2019-01-29" @default.
- W2912706434 modified "2023-09-27" @default.
- W2912706434 title "A new tidy data structure to support exploration and modeling of temporal data" @default.
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