Articles by R on Rob J Hyndman

Simulating from TBATS models

February 17, 2022 | R on Rob J Hyndman

I’ve had several requests for an R function to simulate future values from a TBATS model. We will eventually include TBATS in the fable package, and the facilities will be added there. But in the meantime, if you are using the forecast package an...
[Read more...]

Detecting time series outliers

August 27, 2021 | R on Rob J Hyndman

The tsoutliers() function in the forecast package for R is useful for identifying anomalies in a time series. However, it is not properly documented anywhere. This post is intended to fill that gap. The function began as an answer on CrossValidated and was later added to the forecast package because ...
[Read more...]

Electricity demand data in tsibble format

February 6, 2020 | R on Rob J Hyndman

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 ... [Read more...]

ABS time series as tsibbles

January 23, 2020 | R on Rob J Hyndman

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 ... [Read more...]

Non-Gaussian forecasting using fable

October 16, 2019 | R on Rob J Hyndman

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 example, we will use eating-out ...
[Read more...]

Tidy forecasting in R

September 28, 2019 | R on Rob J Hyndman

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 ...
[Read more...]

Feature-based time series analysis

September 15, 2019 | R on Rob J Hyndman

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. {{...
[Read more...]

Time series graphics using feasts

August 29, 2019 | R on Rob J Hyndman

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 ...
[Read more...]

Tidy time series data using tsibbles

August 28, 2019 | R on Rob J Hyndman

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 Much of the work on these packages has been done by Earo Wang and Mitchell O’...
[Read more...]

You are what you vote

May 16, 2019 | R on Rob J Hyndman

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 th... [Read more...]

forecast 8.5

January 17, 2019 | R on Rob J Hyndman

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(), snai... [Read more...]

Using ggplot2 for functional time series

December 11, 2018 | R on Rob J Hyndman

This week I’ve been attending the Functional Data and Beyond workshop at the Matrix centre in Creswick. I spoke yesterday about using ggplot2 for functional data graphics, rather than the custom-built plotting functionality available in the many functional data packages, including my own rainbow package written with Hanlin Shang. ...
[Read more...]

M4 Forecasting Conference

October 23, 2018 | R on Rob J Hyndman

Following the highly successful M4 Forecasting Competition, there will be a conference held on 10-11 December at Tribeca Rooftop, New York, to discuss the results. The conference will elaborate on the findings of the M4 Competition, with prominent sp...
[Read more...]

MeDaScIn 2018

August 25, 2018 | R on Rob J Hyndman

The annual Melbourne Data Science Initiative (or MeDaScIn, pronounced medicine) is on again next month (24-27 September) with lots of tutorials, and the annual datathon. This year there will be a “Forecasting with R” workshop (25 September) led my two of my Monash colleagues – George Athanasopoulos and Elena Sanina. Another great ... [Read more...]

Saving ts objects as csv files

August 2, 2018 | R on Rob J Hyndman

Occasionally R might not be the tool you want to use (hard to believe, but apparently that happens). Then you may need to export some data from R via a csv file. When the data is stored as a ts object, the time index can easily get lost. So I ... [Read more...]

Seasonal decomposition of short time series

July 13, 2018 | R on Rob J Hyndman

Many users have tried to do a seasonal decomposition with a short time series, and hit the error “Series has less than two periods”. The problem is that the usual methods of decomposition (e.g., decompose and stl) estimate seasonality using at least as many degrees of freedom as there ...
[Read more...]
1 2 3 5

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)