Articles by R on Rob J Hyndman

Monash time series forecasting repository

February 22, 2022 | R on Rob J Hyndman

The Monash time series forecasting respository is a comprehensive collection of time series data made available in a convenient form to encourage empirical forecast evaluations. The repository includes the data from many forecasting competitions includ...

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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...

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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 ...

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Time series cross-validation using fable

May 15, 2021 | R on Rob J Hyndman

Time series cross-validation is handled in the fable package using the stretch_tsibble() function to generate the data folds. In this post I will give two examples of how to use it, one without covariates and one with covariates. Quarterly Austral... [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

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 ... [Read more...]

Non-Gaussian forecasting using fable

October 16, 2019 | R on Rob J Hyndman

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 example, we will use eating-out ...

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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 ...

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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. {{...

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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 ...

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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 tidyverts.org. Much of the work on these packages has been done by Earo Wang and Mitchell O’...

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Poll position: statistics and the Australian federal election

May 25, 2019 | R on Rob J Hyndman

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 ... [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...]

Post-docs in wind and solar power forecasting

February 20, 2019 | R on Rob J Hyndman

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 ... [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. ...

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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...

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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 ...

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Articles by R on Rob J Hyndman

Monash time series forecasting repository

Simulating from TBATS models

Detecting time series outliers

Time series cross-validation using fable

Electricity demand data in tsibble format

ABS time series as tsibbles

Non-Gaussian forecasting using fable

Tidy forecasting in R

Feature-based time series analysis

Time series graphics using feasts

Tidy time series data using tsibbles

Poll position: statistics and the Australian federal election

You are what you vote

Post-docs in wind and solar power forecasting

forecast 8.5

Using ggplot2 for functional time series

M4 Forecasting Conference

MeDaScIn 2018

Saving ts objects as csv files

Seasonal decomposition of short time series

Articles by R on Rob J Hyndman

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