# Time horizon in forecasting, and rules of thumb

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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

*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,

*general*rules of thumb….

- It depends on the model

*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…

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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