Subject ▸ Fpp

Forecasting workshop in Perth

On 26-28 September 2017, I will be running my 3-day workshop in Perth on “Forecasting: principles and practice” based on my book of the same name. Topics to be covered include seasonality and trends, exponential smoothing, ARIMA modelling, dynamic regression and state space models, as well as forecast accuracy methods and forecast evaluation techniques such as cross-validation. Workshop participants are expected to be familiar with basic statistical tools such as multiple regression, but no knowledge of time series or forecasting will be assumed.

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Follow-up forecasting forum in Eindhoven

Last October I gave a 3-day masterclass on “Forecasting with R” in Eindhoven, Netherlands. There is a follow-up event planned for Tuesday 18 April 2017. It is particularly designed for people who attended the 3-day class, but if anyone else wants to attend they would be welcome. Please register here if you want to attend.The preliminary schedule is as follows. 10.00 -- 11.00 New developments in forecasting using R forecast v8.

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Simulating from a specified seasonal ARIMA model

From my email today You use an illustration of a seasonal arima model: ARIMA(1,1,1)(1,1,1)4 I would like to simulate data from this process then fit a model… but I am unable to find any information as to how this can be conducted… if I set phi1, Phi1, theta1, and Theta1 it would be reassuring that for large n the parameters returned by Arima(foo,order=c(1,1,1),seasonal=c(1,1,1)) are in agreement… My answer: Unfortunately arima.

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"Forecasting with R" short course in Eindhoven

I will be giving my 3-day short-course/workshop on “Forecasting with R” in Eindhoven (Netherlands) from 19-21 October.

Details at

Register here

Who's downloading the forecast package?

The github page for the forecast package currently shows the following information Note the downloads figure: 264K/month. I know the package is popular, but that seems crazy. Also, the downloads figure on github only counts the downloads from the RStudio mirror, and ignores downloads from the other 125 mirrors around the world.Here are the top ten downloaded packages from the last month: library(cranlogs) cran_top_downloads(when='last-month') rank package count from to 1 zoo 308290 2015-11-09 2015-12-08 2 forecast 263797 2015-11-09 2015-12-08 3 Rcpp 260636 2015-11-09 2015-12-08 4 lmtest 258810 2015-11-09 2015-12-08 5 fpp 244989 2015-11-09 2015-12-08 6 expsmooth 244179 2015-11-09 2015-12-08 7 fma 243556 2015-11-09 2015-12-08 8 tseries 243172 2015-11-09 2015-12-08 9 stringi 199384 2015-11-09 2015-12-08 10 ggplot2 192072 2015-11-09 2015-12-08 OK, that is very weird.

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Feeling the FPP love

It is now exactly 12 months since the print version of my forecasting textbook with George Athanasopoulos was released on Although the book is freely available online, it seems that a lot of people still like to buy print books.It’s nice to see that it has been getting some good reviews. It is rated 4.6 stars on with 6 out of 8 reviewers giving it 5 stars (the 3 reviewers on Amazon.

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FPP now available as a downloadable e-book

My forecasting textbook with George Athanasopoulos is already available online (for free), and in print via Amazon (for under $40). Now we have made it available as a downloadable e-book via Google Books (for $15.55). The Google Books version is identical to the print version on Amazon (apart from a few typos that have been fixed). To use the e-book version on an iPad or Android tablet, you need to have the Google Books app installed [iPad, Android].

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Resources for the FPP book

The FPP resources page has recently been updated with several new additions including R code for all examples in the book. This was already available within each chapter, but the examples have been collected into one file per chapter to save copying and pasting the various code fragments. Slides from a course on Predictive Analytics from the University of Sydney. Slides from a course on Economic Forecasting from the University of Hawaii.

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Forecasting with R in WA

On 23-25 September, I will be running a 3-day workshop in Perth on “Forecasting: principles and practice” mostly based on my book of the same name. Workshop participants will be assumed to be familiar with basic statistical tools such as multiple regression, but no knowledge of time series or forecasting will be assumed. Some prior experience in R is highly desirable. Venue: The University Club, University of Western Australia, Nedlands WA.

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Errors on percentage errors

The MAPE (mean absolute percentage error) is a popular measure for forecast accuracy and is defined as $$ \text{MAPE} = 100\text{mean}(|y_t - \hat{y}_t|/|y_t|) $$ where $y_t$ denotes an observation and $\hat{y}_t$ denotes its forecast, and the mean is taken over $t$. Armstrong (1985, p.348) was the first (to my knowledge) to point out the asymmetry of the MAPE saying that “it has a bias favoring estimates that are below the actual values”.

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