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Recognition of Patterns With Fractal Structure in Time Series
Abstract
The chapter is focused on an analysis and pattern recognition in time series, which are fractal in nature. Our goal is to find and recognize important Elliott wave patterns which repeatedly appear in the market history for the purpose of prediction of subsequent trader's action. The pattern recognition approach is based on neural networks. Artificial neural networks are suitable for pattern recognition in time series mainly because of learning only from examples. This chapter introduces a methodology that allows analysis of Elliot wave's patterns in time series for the purpose of a trend prediction. The functionality of the proposed methodology was validated in experimental simulations, for whose implementation was designed and created an application environment. In conclusion, all results were evaluated and compared with each other. This chapter is composed only from our published works that present our proposed methodology. We see the main contribution of this chapter in its range, which allows us to present all our published works concerning our proposed methodology together.
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