Electricity demand data in tsibble format

The tsibbledata packages contains the vic_elec data set, containing half-hourly electricity demand for the state of Victoria, along with corresponding temperatures from the capital city, Melbourne. These data cover the period 2012-2014. Other similar data sets are also available, and these may be of interest to researchers in the area. For people new to tsibbles, please read my introductory post.  Australian state-level demand The rawdata for other states are also stored in the tsibbledata github repository (under the data-raw folder), but these are not included in the package to satisfy CRAN space constraints.

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ABS time series as tsibbles

library(tidyverse) library(tsibble) library(readabs) library(raustats) Australian data analysts will know how frustrating it is to work with time series data from the Australian Bureau of Statistics. They are stored as multiple ugly Excel files (each containing multiple sheets) with inconsistent formatting, embedded comments, meta data stored along with the actual data, dates stored in a painful Excel format, and so on. Fortunately there are now a couple of R packages available to make this a little easier.

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Forecasts are always wrong

Recently I was interviewed for the Monash Business School podcast “Thought Capital” on the topic of forecasting. You can listen to the episode here (or read the transcript).

Recent publications

  • D Vicendese, L Te Marvelde, PD McNair, K Whitfield, DR English, S Ben Taieb, RJ Hyndman, R Thomas (2020) Hospital characteristics, rather than surgical volume, predict length of stay following colorectal cancer surgery. Australian and New Zealand Journal of Public Health, 44(1), 73-82. Abstract DOI
  • Earo Wang, Dianne Cook, Rob J Hyndman (2020) Calendar-based graphics for visualizing people's daily schedules. J Computational & Graphical Statistics, to appear. Abstract DOI  pdf
  • George Athanasopoulos, Puwasala Gamakumara, Anastasios Panagiotelis, Rob J Hyndman and Mohamed Affan (2020) Hierarchical forecasting. Macroeconomic forecasting in the era of big data, ed. P. Fuleky, Springer, Chapter 21, pp.689-719. Abstract DOI  pdf
  • Jeremy Forbes, Dianne Cook, Rob J Hyndman (2020) Spatial modelling of the two-party preferred vote in Australian federal elections: 2001-2016. Australian and New Zealand Journal of Statistics, to appear. Abstract  pdf
  • Earo Wang, Di Cook and Rob J Hyndman (2020) A new tidy data structure to support exploration and modeling of temporal data. Journal of Computational & Graphical Statistics, to appear. Abstract DOI  pdf

Recent and upcoming seminars

  • How Rmarkdown changed my life. (30 January 2020) More info...
  • Tidy time series & forecasting in R. (27 January 2020) More info...
  • The journal game. (29 October 2019) More info...
  • Tidy time series analysis in R. (27 September 2019) More info...
  • Feature-based time series analysis. (27 September 2019) More info...