TRN: picking up the challenge of non lin earity testing by means of Topology Representing Networks,

In this work, I will describe a new approach for time series non linearity testing by means of neural networks, and I'll extend it to financial data. The novelty of this approach stands primarily in the kind of artificial agents chosen for simulations: Topology Representing Networks (TRN), that is competitive learning algorithms. In this context, a TRN ensemble will be used to analyse signals generated by different processes: periodic and deterministic, uniformly distributed and multi-scaling L-stable processes. The performances obtained by means of this technique will be compared to more conventional tools in time series analysis, with particular attention to recurrence quantification analysis. Furthermore, real world data will be observed and the results obtained by TRN will be closely linked with economical interpretations