Subject ▸ Teaching

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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"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

The bias-variance decomposition

This week, I am teaching my Business Analytics class about the bias-variance trade-off. For some reason, the proof is not contained in either ESL or ISL, even though it is quite simple. I also discovered that the proof currently provided on Wikipedia makes little sense in places. So I wrote my own for the class. It is longer than necessary to ensure there are no jumps that might confuse students.

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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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Student forecasting awards from the IIF

At the IIF annual board meeting last month in Rotterdam, I suggested that we provide awards to the top students studying forecasting at university level around the world, to the tune of $100 plus IIF membership for a year. I’m delighted that the idea met with enthusiasm, and that the awards are now available. Even better, my second year forecasting subject has been approved for an award. The IIF have agreed to fund awards for 20 forecasting courses to start with.

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Creating a handout from beamer slides

I’m about to head off on a speaking tour to Europe (more on that in another post) and one of my hosts has asked for my powerpoint slides so they can print them. They have made two false assumptions: (1) that I use powerpoint; (2) that my slides are static so they can be printed. Instead, I produced a cut-down version of my beamer slides, leaving out some of the animations and other features that will not print easily.

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Interpreting noise

When watching the TV news, or reading newspaper commentary, I am frequently amazed at the attempts people make to interpret random noise. For example, the latest tiny fluctuation in the share price of a major company is attributed to the CEO being ill. When the exchange rate goes up, the TV finance commentator confidently announces that it is a reaction to Chinese building contracts. No one ever says “The unemployment rate has dropped by 0.

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Fast computation of cross-validation in linear models

The leave-one-out cross-validation statistic is given by $$ \text{CV} = \frac{1}{N} \sum_{i=1}^N e_{[i]}^2, $$ where ${e_{[i]} = y_{i} - \hat{y}_{[i]}} $, the observations are given by $y_{1},\dots,y_{N}$, and $\hat{y}_{[i]}$ is the predicted value obtained when the model is estimated with the $i\text{th}$ case deleted. This is also sometimes known as the PRESS (Prediction Residual Sum of Squares) statistic. It turns out that for linear models, we do not actually have to estimate the model $N$ times, once for each omitted case.

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