In the previous post I showed that price fluctuations have a gaussian distribution. This has a similarity with gaussian noise. Apparently, there are several ways to predict gaussian time series. One is described here:
But it involves concepts which are too advanced and complicated for me at this moment. Besides: gaussian processes are not the same as gaussian noise. It seems that a gaussian process is formally defined as a process involving "multivariate normal distribution". Since the price fluctuations I'm studying have only one variable, I believe using gaussian process prediction methods would be overkill. I might be wrong, though.
Another method is one used to filter out gaussian noise. It's called "Kalman filtering". Kalman filtering seems useful, because it works by predicting gaussian noise in order to eliminate it. Because currency price fluctuations follow a gaussian distribution, I think the predictive component of the Kalman filter may be useful in predicting short-term future fluctuations in currency price.
The following seems like a good, comprehensive, introduction to Kalman filtering:
So the basic idea is to treat FOREX price fluctuation as if it were gaussian noise, and try to predict it short-term with a Kalman filter.
I want to be able to predict short-term future price fluctuations because I discovered that sudden and large price decrements produce important losses when using my rustic buy-and-hold algorithm. These predictions may turn out to be useful in setting up an effective predictive stop-loss alarm for my rustic buy-and-hold algorithm.