When you get the patterns into the wave observation, you will see the cycles. Cycles that expand and contract, that generate trends, cycles of any dimension. Cycles that change continuously. Unlike technical analysis that tries to avoid the so-called “noise”, neural networks love noise, you feed them with noise, the rawest data as possible seems always better, and then you have to train them.

I know at least three methods to extract cycles from a historical sequence: the Fourier’s Transforms and the method of Armstrong. Well, in effect I know almost nothing about Fourier’s Transforms and not enough of the Armstrong method. But I know about another way: the neural networks. What is interesting is that all three methods are totally different, use different tools and apply different logic.

Training a neural network means transferring knowledge to the data. You associate, tag, mark, you name it, a certain record in your database to a certain “meaning”. For example, to the days lacking to the next turn. The unique ability of neural networks is to project values in the future, not based on an abstract theory, but crunching the real numbers.

Artificial Intelligence can analyze cycles, you just have to pose the correct question, as to the Speaking Mirror: you need to extract meaning from the patterns. One of the r.Virgeel’s indicators, the rv.NC Next Cycle evaluates when the next cycle will take place: how many bars into the future. It is not a triggering indicator, it is aimed to focus our attention on the incoming events. I consider the rv.NC an alert indicator.