Articles by Rob J Hyndman

My forecasting book now on Amazon

April 8, 2014 | Rob J Hyndman

For all those people asking me how to obtain a print version of my book “Forecasting: principles and practice” with George Athanasopoulos, you now can. Order on Amazon.com Order on Amazon.co.uk Order on Amazon.fr The online book will continue to be freely available. The print version ... [Read more...]

Job at Center for Open Science

April 6, 2014 | Rob J Hyndman

This looks like an interesting job. Dear Dr. Hyndman, I write from the Center for Open Science, a non-profit organization based in Charlottesville, Virginia in the United States, which is dedicated to improving the alignment between scientific values and scientific practices. We are dedicated to open source and open science. ... [Read more...]

Cover of my forecasting textbook

March 18, 2014 | Rob J Hyndman

We now have a cover for the print version of my forecasting book with George Athanasopoulos. It should be on Amazon in a couple of weeks. The book is also freely available online. This is a variation of the most popular one in the poll conducted a mon... [Read more...]

Fast computation of cross-validation in linear models

March 17, 2014 | Rob J Hyndman

The leave-one-out cross-validation statistic is given by     where , are the observations, and is the predicted value obtained when the model is estimated with the 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 ... [Read more...]

Testing for trend in ARIMA models

March 12, 2014 | Rob J Hyndman

Today’s email brought this one: I was wondering if I could get your opinion on a particular problem that I have run into during the reviewing process of an article. Basically, I have an analysis where I am looking at a couple of time-series and I wanted to know ... [Read more...]

Unit root tests and ARIMA models

March 12, 2014 | Rob J Hyndman

An email I received today: I have a small problem. I have a time series called x : - If I use the default values of auto.arima(x), the best model is an ARIMA(1,0,0) - However, I tried the function ndiffs(x, test=“adf”) and ndiffs(x, test=“kpss”) as ... [Read more...]

Using old versions of R packages

March 9, 2014 | Rob J Hyndman

I received this email yesterday: I have been using your ‘forecast’ package for more than a year now. I was on R version 2.15 until last week, but I am having issues with lubridate package, hence decided to update R version to R 3.0.1. In our organization even getting an open source ... [Read more...]

Forecasting weekly data

March 4, 2014 | Rob J Hyndman

This is another situation where Fourier terms are useful for handling the seasonality. Not only is the seasonal period rather long, it is non-integer (averaging 365.25/7 = 52.18). So ARIMA and ETS models do not tend to give good results, even with a period of 52 as an approximation. Regression with ARIMA errors The ... [Read more...]

Fitting models to short time series

March 3, 2014 | Rob J Hyndman

Following my post on fitting models to long time series, I thought I’d tackle the opposite problem, which is more common in business environments. I often get asked how few data points can be used to fit a time series model. As with almost all sample size questions, there ... [Read more...]

More time series data online

February 27, 2014 | Rob J Hyndman

Earlier this week I had coffee with Ben Fulcher who told me about his online collection comprising about 30,000 time series, mostly medical series such as ECG measurements, meteorological series, birdsong, etc. There are some finance series, but not ma... [Read more...]

The forecast mean after back-transformation

February 24, 2014 | Rob J Hyndman

Many functions in the forecast package for R will allow a Box-Cox transformation. The models are fitted to the transformed data and the forecasts and prediction intervals are back-transformed. This preserves the coverage of the prediction intervals, and the back-transformed point forecast can be considered the median of the forecast ... [Read more...]

Forecasting within limits

February 21, 2014 | Rob J Hyndman

It is common to want forecasts to be positive, or to require them to be within some specified range . Both of these situations are relatively easy to handle using transformations. Positive forecasts To impose a positivity constraint, simply work on the log scale. With the forecast package in R, this ... [Read more...]

Backcasting in R

February 19, 2014 | Rob J Hyndman

Sometimes it is useful to “backcast” a time series — that is, forecast in reverse time. Although there are no in-built R functions to do this, it is very easy to implement. Suppose x is our time series and we want to backcast for periods. Here is some code that should ... [Read more...]

Global energy forecasting competitions

February 19, 2014 | Rob J Hyndman

The 2012 GEFcom competition was a great success with several new innovative forecasting methods introduced. These have been published in the IJF as follows: Hong, Pinson and Fan. Global Energy Forecasting Competition 2012 Charleton and Singleton. A refined parametric model for short term load forecasting Lloyd. GEFCom2012 hierarchical load forecasting: Gradient boosting ... [Read more...]

Hierarchical forecasting with hts v4.0

February 12, 2014 | Rob J Hyndman

A new version of my hts package for R is now on CRAN. It was completely re-written from scratch. Not a single line of code survived. There are some minor syntax changes, but the biggest change is speed and scope. This version is many times faster than the previous version ... [Read more...]

Detecting seasonality

February 7, 2014 | Rob J Hyndman

I occasionally get email asking how to detect whether seasonality is present in a data set. Sometimes the period of the potential seasonality is known, but in other cases it is not. I’ve discussed before how to estimate an unknown seasonal period, and how to measure the strength of ... [Read more...]

Feedback on OTexts covers please

February 5, 2014 | Rob J Hyndman

We are currently selecting the cover design for OTexts books. The first one to go into print will be Forecasting: principles and practice. We have narrowed the choice to the two designs below, although changes are still possible. I thought it would be useful to get some feedback on these ... [Read more...]

Interview for the Capital of Statistics

February 4, 2014 | Rob J Hyndman

Earo Wang recently interviewed me for the Chinese website Capital of Statistics. The English transcript of the intervew is on Earo’s personal website. This is the third interview I’ve done in the last 18 months. The others were for: Data Mining Research. Republished in Amstat News. DecisionStats.   [Read more...]

Computational Actuarial Science with R

February 2, 2014 | Rob J Hyndman

I recently co-authored a chapter on “Prospective Life Tables” for this book, edited by Arthur Charpentier. R code to reproduce the figures and to complete the exercises for our chapter is now available on github. Code for the other chapters should also be available soon. The book can be pre-ordered ... [Read more...]

Automatic time series forecasting in Granada

January 30, 2014 | Rob J Hyndman

In two weeks I am presenting a workshop at the University of Granada (Spain) on Automatic Time Series Forecasting. Unlike most of my talks, this is not intended to be primarily about my own research. Rather it is to provide a state-of-the-art overview of the topic (at a level suitable ... [Read more...]
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