Most of us have a general knowledge of what statistic does: it extracts from data some relevant information about the data itself. What a neural network does is to add a layer of correlation between the data and relate it to the desired output. You must instruct a neural network, before using it. You must associate one or more values to each of your info sets (records of a table in a database, usually). This is a process of knowledge transfer. It works well with classifications to produce diagnosis systems.
When well taught, and it is a long and delicate task indeed, the neural network has the ability to recognize patterns in never seen before data and so output the less improbable evaluation from its data bank of knowledge.
What are patterns? A pattern is an event that repeats in different shapes, but always in the same manner, staging always similar processes. The concept of pattern was introduced by the architect Christopher Alexander in its A Pattern Language (Oxford University Press, 1977). It was aimed for architects, and you may consider the approach with this sample 125 STAIR SEATS.
Then the concept of pattern has rapidly spread into the world of software programming, producing the revolution of object-oriented languages (OOPL). Leveraging on the human relational attitudes, patterns rise in every context, not excluded trading and investing, as traders know well. If you use technical indicators, you know what I’m talking about: your eyes are trained to recognize patterns in the charts. That’s why they call it artificial intelligence.