Artificial Neural Networks (ANNs) are biologically inspired. Specifically, they borrow ideas from the manner in which the human brain works. The human brain is composed of special cells called neurons. Estimates of the number of neurons in a human brain cover a wide range (up to 150 billion), and there are more than a hundred different kinds of neurons, separated into groups called networks. Each network contains several thousand neurons that are highly interconnected. Thus, the brain can be viewed as a collection of neural networks.

Today’s ANNs, whose application is referred to as neural computing, use a very limited set of concepts from biological neural systems. An artificial neural network (ANN), usually called “neural network” (NN), is a mathematical model or computational model that tries to simulate the structure and/or functional aspects of biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learningphase. Neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.

Predicting is making claims about something that will take place, often based on  information from past and from current state. Everyone solves the problem of prediction every day with various degrees of success. For example weather, harvest, energy consumption, movements of forex (foreign exchange) currency pairs or of shares of stocks, earthquakes, and a lot of other stuff needs to be predicted.

In technical analysis predictable parameters of a system can be often be expressed and evaluated using equations – prediction is then simply evaluation or solution of such equations. However, practically we face problems where such a description would be too complicated or not possible at all. In addition, the solution by this method could be very complicated computationally, and sometimes we would get the solution after the event to be predicted happened.

It is possible to use various approximations, for example degeneration of the dependency of the predicted variable on other events that is then extrapolated to the future. Finding such approximation can be also difficult. This approach generally means creating the model of the predicted event.

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