Blog Archives

Errors on percentage errors

April 16, 2014
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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     where denotes an observation and denotes its forecast, and the mean is taken over . 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...

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My forecasting book now on Amazon

April 8, 2014
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My forecasting book now on Amazon

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 of the book is intended to help fund the development of the OTexts...

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Job at Center for Open Science

April 6, 2014
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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. We are reaching out to you to find out...

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Cover of my forecasting textbook

March 18, 2014
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Cover of my forecasting textbook

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...

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Fast computation of cross-validation in linear models

March 17, 2014
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Fast computation of cross-validation in linear models

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 not actually have to estimate the...

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Testing for trend in ARIMA models

March 12, 2014
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Testing for trend in ARIMA models

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 if, over time there was an upward trend in the...

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Unit root tests and ARIMA models

March 12, 2014
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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 the KPSS test seems to be the default value, and the number of difference...

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Using old versions of R packages

March 9, 2014
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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 application require us to go through a...

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Forecasting weekly data

March 4, 2014
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Forecasting weekly data

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 simplest approach is a regression...

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Fitting models to short time series

March 3, 2014
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Fitting models to short time series

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 is no easy answer. It depends on the number of model parameters...

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