Time series forecasting

Hi Perry,

my impression after a very cursory glance: this looks like noise. 
Perhaps you should think a little more about your series - what kind of 
seasonality there could be (is this weekly data? or monthly?), whether 
the peaks and troughs could be due to some kind of external driver, 
whether you really have count data, that kind of thing.

Until then, there is little else to do than to use a very simple method, 
e.g. forecast the last observation (random walk) or the mean of the 
observations (white noise), or the median. All of these "benchmarks" can 
be surprisingly hard to beat.

If you have seasonality but no external influence, you could look at 
smoothing methods, they are nice to interpret and usually perform very 
well. I'd recommend Hyndman et al., "Forecasting with Exponential 
Smoothing: The State Space Approach" and the accompanying forecast R 
package, mainly with the ets() function.

You could also look at arima(). I fitted an ARIMA model to your data, 
and as expected, it returned a simple mean (not that I would recommend 
blindly fitting ARIMA to just any data):

Call:
arima(x = foo[, 2])

Coefficients:
       intercept
          7.2333
s.e.     0.8009

sigma^2 estimated as 19.25:  log likelihood = -86.93,  aic = 177.85

And for count data, you could use some variants of ARIMA, e.g., INAR.

HTH,
Stephan

pgary at gol.com schrieb:

Time series forecasting

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