Revenue Forecast and the secret of the last observed value

March 13, 2016
By

(This article was first published on Copula, and kindly contributed to R-bloggers)

I’ve tried a few forecasts with the Forecast Package but irrespective of the number of observations it appears that the last observed value has a dramatic impact on the results.

Just a short example with a few observations. This are revenues of an IT&S company with no seasonality impact.

The company had a fantastic 2015 result but in the recent history the numbers showed some volatility.

Hence I was wondering what the Forecast Package would come up with.

a=ts(c(3006,3658,3712,3575,3228,3845,4362), frequency = 1, start = c(2008, 2))
plot(forecast(a,h=5,level=c(80,95)),main=”XY 5 Year Forecast”)

Now, let’s assume the company did improve the numbers in 2015 but just on a more modest basis to let’s say 3900.

a=ts(c(3006,3658,3712,3575,3228,3845,3900), frequency = 1, start = c(2008, 2))
plot(forecast(a,h=5,level=c(80,95)),main=”XY 5 Year Forecast”)

 Now, have a look at the 5 year Forecast. Revealing, isn’t it!

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