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 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...]

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...]

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...]

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 ...

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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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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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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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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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...]

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...]

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...]

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...]

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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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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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...]

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...]

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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Forecasting benchmarks are very important when testing new forecasting methods, to see how well they perform against some simple alternatives. Every week I get sent papers proposing new forecasting methods that fail to do better than even the simplest benchmark. They are rejected without review.
Typical benchmarks include the naï...

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First semester teaching is nearly finished, and that means conference season for me. Here are some talks I’m giving in the next two months. Click the links for more details.
Melbourne, Australia. 28 May: Panel discussion: Forecasting models, the uncertainties and associated risk
Boulder, Colorado, USA. 17-20 June: International Symposium ... [Read more...]

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