# Is it possible to do an adequate prediction on tiny sample?

I have a financial time series, x, it's length is n=8 observations only. Each observation corresponds to the quarterly costs (numerical value) of a firm. I need to predict future costs and find 95% confidence interval on the next quarter.

x <-    c(1122156.70, 777243.30, 741537.90, 1160976.40,
1316723.00, 781010.00, 70447.00, 1413481.00)
plot(x, xlab='Quarters', ylab='Cost, USD')


From the plot you can assume in this series that there exists a seasonal component.

My intuition is: to split the quarterly value on the month one, and then apply some method (for example non-linear regression) to predict future costs. For simplicity let's split under assumption of the uniform distibution. For instance,

x1 <-rep(x/3, each=3) # uniform split on 3
length(x)
#[1] 8
length(x1)
#[1] 24


In this case I'll have $n=24$ observations.

Of course you can say it's impossible to do an adequate prediction on such a tiny sample.

Question. Could you please share your point of view on the problem?

• Could you clarify what you expect to gain by "splitting" the quarterly data into months? You don't really have 24 observations at that point because you really haven't obtained any additional information. The assumption that each month in a seasonal, quarterly financial series is constant is also unlikely to be true, in any case. Nov 12, 2016 at 1:57
• Thanks for the comment. I hope to increase a size sample. Uniform splitting is the assumption only. I can split into three unequal terms which sum equals to the quartel value,
– Nick
Nov 12, 2016 at 2:46