All Hyndsight posts by date

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 graphics for tsibbles. (See my first post for a description of tsibbles.

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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 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, providing the data infrastructure for tidy temporal data with wrangling tools.

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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 2019. Room G03, Learning and Teaching Building, 19 Ancora Imparo Way, Clayton Campus, Monash University

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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 accessible version of our analysis.

Translations of "Forecasting: principles and practice"

There are now translations of my forecasting textbook (coauthored with George Athanasopoulos) into Chinese and Korean. The Chinese translation was produced by a team led by Professor Yanfei Kang (Beihang University) and Professor Feng Li (Central University of Finance and Economics). The following students were also involved: Cheng Fan, Liu Yu, Long Xiaoyu, Wang Xiaoqian, Zeng Jiayue, Zhang Bohan, and Zhu Shuaidong. The Korean translation was produced by Dr Daniel Young Ho Kim.

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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 R and/or Python Solid background in Machine Learning and/or Statistics Background in time series forecasting desirable Solar power forecasting: 6 months contract Good programming skills in R and/or Python Solid background in Machine Learning and/or Statistics Data will be cloud coverage data from sky cams, so some image processing background is necessary Background in time series forecasting desirable Please contact Christoph Bergmeir if you are interested.

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Advice to PhD applicants

For students who are interested in doing a PhD at Monash under my supervision. First, check that you satisfy the following criteria: You must have completed a degree in statistics that involved some research component (e.g., an honours or masters thesis). A degree in computer science, mathematics or econometrics might be acceptable if it contained a substantial amount of statistics. A degree in any other field is not sufficient background to work with me.

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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() unit root tests. Improved auto.arima() stepwise search algorithm (some neighbouring models were missed previously). We haven’t done a major release for two years, and there is unlikely to be another one now.

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Network for early career researchers in forecasting

The International Institute of Forecasters has established interest group sections, devoted to specialized domains of forecasting. One of the first such sections will be for early career researchers. So if you are a PhD student, post-doc, or otherwise a relatively junior researcher working in forecasting, this is for you! The first events will be during the ISF in Thessaloniki in June 2019, including the following: ECR welcoming event. A meet and greet event prior to the ISF welcome reception.

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Why doesn't auto.arima() return the model with the lowest AICc value?

This question seems to come up frequently on or in my inbox. I have this time series, however it yields different results when I use the auto.arima and Arima functions. library(forecast) xd <- ts(c(23786, 25955, 54373, 21561, 14552, 13284, 12714, 11821, 15445, 21307, 17228, 20007, 23065, 32811, 43147, 15127, 13497, 12224, 11412, 11888, 14210,18978, 15782, 17216, 16417, 22861, 42616, 17057, 9741, 10503, 7170, 10686, 9762, 15773, 15280, 13212, 14784, 26104, 29947), frequency = 12, start=c(2014,1), end=c(2017,3)) fit1 <- auto.

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