Time horizon in forecasting, and rules of thumb

April 7, 2011
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(This article was first published on Freakonometrics - Tag - R-english, and kindly contributed to R-bloggers)

I recently received an email about forecasting and rules of thumb. "Dans la profession [...] se transmet une règle empirique qui voudrait que l'on prenne un historique du double de l'horizon de prévision : 20 ans de données pour une prévision à 10 ans, etc... Je souhaite savoir si cette règle n'aurait pas, par hasard, un fondement théorique quitte à ce que le rapport ne soit pas de 2 pour 1, mais de 3 pour 1 ou de 1 pour 1 par exemple." To summarize briefly, the rule is to consider a 2-1 ratio for the period of observation vs. forecast horizon. And the interesting question is if there are justifications for such a rule...

At first, I remembered a rules of thumb, from the book by Box and Jenkins, which states that it is meaningless to look at autocorrelations when lags exceed the sample size over 6. So with 12 years of data, autocorrelations with a lag higher than two years are useless. But it is not what is mentioned here. So I looked at some dataset, and some standard time series models.

  • It depends on the series
It might obvious... but if it is the case, it means that it will be difficult to have a general rule of thumb. Consider e.g. the number of airline passengers,
library(forecast)
X = AirPassengers
ETS = ets(X)
plot(forecast(ETS,h=length(X)/2))
or some sales in a big store,

or car casualties in France, or the temperature in Nottingham Castle,

or the water level at Lack Hurron, or the flow of the Nile river,

or see also here for forecasting techniques in demography. Actually, in the case of life insurance, actuaries have to forecast future demography, i.e. try to assess death rates of those who currently purchase retirement contracts, who might be 20 years old. So they have to forecast death rate until 2100, say. One the one hand, it sounds difficult to make forecast over a century (it is already difficult for climate, I guess it is even more complex for human life). On the other hand, a 2-1 ratio means that we have to use data from 1800... Here again, it is difficult to justify that mortality in the 1850 could be interesting to say anything about mortality in 2050. So I guess it will be difficult to justify the use of general rules of thumb....
  • It depends on the model
Consider the following (simulated) series. Several models can be fitted. And the shape on the forecast (and the forecast error) will depend on the model considered. The benchmark can be the model without any dynamics, i.e. we assume that observations are i.i.d. Or more classically, assume that it is simple a white noise, i.e. an i.i.d centered process. Then the forecast is the following,

With that kind of assumption, we see that the 2-1 ratio is useless since we can get forecasts up to any horizon.... But that does not seem very robust. For instance, if we consider exponential smoothing techniques, we can obtain

Which is rather different. And with the 2-1 ratio, obviously, there is a lot of uncertainty at the end ! It would be even worst if we assume that we look at a random walk. Because actually a dozen models - at least - can be considered, from ARIMA, seasonal ARIMA, Holt Winters, Exponential Smoothing, etc...
http://i2.wp.com/freakonometrics.blog.free.fr/public/perso2/animationforecast.gif?w=456
So I do not see any theoretical justification of that rule of thumb. Obviously, the maximum horizon can not be extremely far away if the series is non-stationary, with a very irregular pattern, and with a lot of noise... So we're back at the beginning. If anyone is willing to share his or her experience, comments are open.

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