Time series can be frustrating to work with, particularly when processing raw data into model-ready data. This work presents two new packages that address a gap in existing methodology for time series analysis (raised in rstudio::conf 2018). The tsibble package supports organizing and manipulating modern time series, leveraging tidy data principles along with contextual semantics: index and key. The tsibble data structure seamlessly flows into forecasting routines. The fable package is a tidy renovation of the forecast package. It promotes transparent forecasting practices and concise model representations, to empower analysts tackling a broad domain of forecasting problems. This collection of packages form the tidyverts, which facilitates a fluent and fluid workflow for analyzing time series.
I’m currently doing my Ph.D. on statistical visualisation of temporal-context data at Monash University, supervised by Professor Di Cook and Professor Rob J Hyndman. I enjoy developing open-source tools with R, and is the (co)author of some widely-used R packages including anomalous, hts, sugrrants, rwalkr and tsibble. My research areas invovle data visualisation, time series analysis, and computational statistics.
