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.

**Contents**hide

#### Best Answer

Jason,

Holt-Winters is a particular model form, normally additive or multiplicative and apparently may not be applicable to your particular time series. In general a Transfer Function incorporating both stochastic and deterministic structure has been found to a powerful way of handling problems like this. The problem you face I believe may be better handled with a mixed frequency model that might include one or more trends and/or level shifts and perhaps either weekly or monthly effects to deal with the "seasonal component" . Furthermore the model might exclude any identifiable anomalies so as to get a more robust "signal". I suggest that you post your original data to the web and have some of the readers use their methods/software to try to model the data. I will try and do the same. It might make for a very interesting comparison !