Forecasting COVID-19

What makes forecasting hard? Forecasting pandemics is harder than many people think. In my book with George Athanasopoulos, we discuss the contributing factors that make forecasts relatively accurate. We identify three major factors: how well we understand the factors that contribute to it; how much data is available; whether the forecasts can affect the thing we are trying to forecast. For example, tomorrow’s weather can be forecast relatively accurately using modern tools because we have good models of the physical atmosphere, there is tons of data, and our weather forecasts cannot possibly affect what actually happens.

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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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Recent publications

  • Han Li, Rob J Hyndman (2020) Assessing Longevity Inequality in the US: What Can Be Said About the Future?. Abstract  pdf
  • Souhaib Ben Taieb, James W Taylor, Rob J Hyndman (2020) Hierarchical Probabilistic Forecasting of Electricity Demand with Smart Meter Data. J American Statistical Association, to appear. Abstract DOI  pdf
  • Sevvandi Kandanaarachchi, Mario A Muñoz, Rob J Hyndman and Kate Smith-Miles (2020) On normalization and algorithm selection for unsupervised outlier detection. Data Mining and Knowledge Discovery, 34, 309-354. Abstract DOI  pdf
  • 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

Recent and upcoming seminars

  • Developing good research habits. (31 March 2020) More info...
  • 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...
  • Forecasts are always wrong (but we need them anyway). (9 October 2019) More info...