All Hyndsight posts by date
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 tutorial will be led by Steph Kovalchik (from Tennis Australia) on sports analytics with R (24 September).
For the full list of tutorials, see the MeDaScIn website.
Next week is National Science Week and there are a few mathematics activities happening around Melbourne that are being sponsored by ACEMS.
Elsewhere in Melbourne: Mon 13 Aug 2018, 6:00pm - 7:30pm
Public Talk: Is this your card?
Location: University of Melbourne
Speakers: Anthony Mays & Jen Palisse
Pick a card, any card! The immortal phrase of the magician. In this talk, we’ll look at some great card tricks that have simple maths behind them.
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 wrote a little function to make this easier, using the tsibble package to do almost all of the work in looking after the time index.
All talks from useR!2018, held in Brisbane last week, are now available on YouTube.
Links to talks from members of my research team are given below.
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 are seasonal periods. So you need at least two observations per seasonal period to be able to distinguish seasonality from noise.
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 naïve method (especially for finance and economic data), the seasonal naïve method (for seasonal data), an automatically selected ETS model, and an automatically selected ARIMA model.
In late June, I will be in New York to teach my 3-day workshop on Forecasting using R. Tickets are available at Eventbrite.
This is the first time I’ve taught this workshop in the US, having previously run it in the Netherlands and Australia. It will be based on the 2nd edition of my book “Forecasting: Principles and Practice” with George Athanasopoulos. All participants will get a print version of the book.
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 on Forecasting. I’ll be talking about “Tidy forecasting in R”. New York, USA. 21 June: Feature-based time series analysis. New York Open Statistical Programming Meetup, eBay NYC.
The latest version of the forecast package for R is now on CRAN. This is the version used in the 2nd edition of my forecasting textbook with George Athanasopoulos. So readers should now be able to replicate all examples in the book using only CRAN packages.
A few new features of the forecast package may be of interest. A more complete Changelog is also available.
mstl() handles multiple seasonality STL decomposition was designed to handle a single type of seasonality, but modern data often involves several seasonal periods (e.
Prediction competitions are now so widespread that it is often forgotten how controversial they were when first held, and how influential they have been over the years.
To keep this exercise manageable, I will restrict attention to time series forecasting competitions — where only the history of the data is available when producing forecasts.
Nottingham studies The earliest non-trivial study of time series forecast accuracy was probably by David Reid as part of his PhD at the University of Nottingham (1969).