Publications by R on Rob J Hyndman
forecast 8.5
The latest minor release of the forecast package has now been approved on CRAN and should be available in the next day or so. Version 8.5 contains the following new features Updated tsCV() to handle exogenous regressors. Reimplemented naive(), snaive(), rwf() for substantial speed improvements. Added support for passing arguments to auto.arima()...
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Post-docs in wind and solar power forecasting
We currently have two postdoc opportunities together with an industry partner in the field of wind and solar power forecasting (full time, Level B). They are suitable for recently graduated PhD students that can start between now and June-July. The opportunities are as follows: Wind power forecasting: 1 year contract Good programming skills in ...
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You are what you vote
I’ve tried my hand at writing for the wider public with an article for The Conversation based on my paper with Di Cook and Jeremy Forbes on “Spatial modelling of the two-party preferred vote in Australian federal elections: 2001-2016”. With the next Australian election taking place tomorrow, we thought it was timely to put out a publicly ac...
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Poll position: statistics and the Australian federal election
One of the few people in Australia who did not write off a possible Coalition win at the recent federal election was Peter Ellis. We’ve invited him to come and give a talk about making sense of opinion polls and the Australian federal election on Friday this week at Monash University. Visitors are welcome. Here are the details. 11am, 31 May 201...
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Tidy time series data using tsibbles
There is a new suite of packages for tidy time series analysis, that integrates easily into the tidyverse way of working. We call these the tidyverts packages, and they are available at tidyverts.org. Much of the work on these packages has been done by Earo Wang and Mitchell O’Hara-Wild. The first of the packages to make it to CRAN was tsibble,...
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Time series graphics using feasts
This is the second post on the new tidyverts packages for tidy time series analysis. The previous post is here. For users migrating from the forecast package, it might be useful to see how to get similar graphics to those they are used to. The forecast package is built for ts objects, while the feasts package provides features, statistics and gra...
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Feature-based time series analysis
In my last post, I showed how the feasts package can be used to produce various time series graphics. The feasts package also includes functions for computing FEatures And Statistics from Time Series (hence the name). In this post I will give three examples of how these might be used. library(tidyverse) library(tsibble) library(feasts) Exploring...
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Tidy forecasting in R
The fable package for doing tidy forecasting in R is now on CRAN. Like tsibble and feasts, it is also part of the tidyverts family of packages for analysing, modelling and forecasting many related time series (stored as tsibbles). For a brief introduction to tsibbles, see this post from last month. Here we will forecast Australian tourism data by...
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Non-Gaussian forecasting using fable
library(tidyverse) library(tsibble) library(lubridate) library(feasts) library(fable) In my previous post about the new fable package, we saw how fable can produce forecast distributions, not just point forecasts. All my examples used Gaussian (normal) distributions, so in this post I want to show how non-Gaussian forecasting can be done. As an e...
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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 alo...
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