Predicting temperature time series with Holt-Winters

I am trying to write a prediction algorithm for a set of temperature data. I settled on Holt-Winters since it seemed to be a simple time series prediction algorithm and I can easily code it up in python to understand what is going on with it.

When I am plotting the smoothing function as it learns this is how it looks. As you can see, it follows the original curve pretty well.

But when I try to plot a future curve for one year (365 days) -- it really falls down and dies.

And to me intuitively it makes sense why it dies like that. Because if you see the last prediction equation of Holt-Winters it really only makes use of the very very last point in both the curve smoothing and the trend smoothing. And we know that exponential smoothing has a very short memory because of the whole exponential thing.

So I am wondering how does one actually go about using Holt-Winters for prediction (specifically for 365 day seasonal data like this).

If you know of any other methods which I can look at for this domain (temperature prediction) please let me know. I come from python, so it would be very useful if you can point me to resources such as libraries etc to get the job done.

• Are you confusing Holt with Holt-Winters? Holt-Winters adds a seasonality equation which reflects a time offset of $s$ (the seasonality), which in your case would be 365. At least that's how I understand Holt-Winters. – Wayne Dec 20 '11 at 18:24