Neural Net is a mathematical procedure of modelling existing processes. Processing complex data and deriving specific patterns from that, the Neural Net creates a model that is able to mimic the original process and thus to make conclusions about its future outcome. An example of this process is weather forecast. We can collect data about the air (such as humidity, temperature, density, etc.) and we can also assume that the weather today strongly depends on the weather conditions yesterday and the days before yesterday. It means that we expect the existence of the same patterns between today’s data and the previous information. The process of finding these patterns is what Neural Net is designed to do: it takes a number of factors into account and observes how these factors might affect the result.
This concept is also illustrated in the picture below:
Training of the Neural Network is a process of adjustment of the NN using available historical data (the information that we already know). Back to our example, the NN takes as an output the weather condition today (that we know) and considers it in regards to inputs (which are the weather conditions yesterday, two days ago, etc.; we know them too). The NN corrects itself to get better accuracy between the “forecast” (output) and already known weather. Then we take the weather yesterday as a new output while inputs are the weather two, three, etc. days ago – and correct the NN again. Thus we get the best coincidence between the forecasted by this NN and real weather conditions
These are not Holy Grail but given you a projection line which should be used as per proper Risk and Money Management