Frontiers in Signal Processing

5

Authors: Narek Abroyan

During the last several years machine learning started to revolutionize many industrial fields by replacing human intellectual work with recent technologies. Machine learning has started to be used in financial sphere as well for predicting stock prices, detecting fraud actions etc. In this work, we are focusing on financial market risk classification, which is a part of fraud action detection problem. Although artificial intelligence researchers and specialists achieved notable results in visual, voice signal and natural language processing tasks by using new methods and approaches of deep learning, such as convolutional and recurrent neural networks, not many results are in the sphere of elaboration of real-time non-stationary data, such as financial data. Moreover, methods which are used in industry usually are not published. The goal of this work is exploring, experimenting and providing new and more effective methods of classification of financial non-stationary risk data by using neural networks.

14

Authors: Sarkis Agaian, John T. Gill III

The Golden ratio has played an important role in musical composition, architecture, visual art, science, and increasingly in signal processing [1,2,3]. Underlying many of these applications are several extensions of the golden proportions including the Golden p-Section by Stakhov, the generalized Golden section by Bradley, and others [4,5]. In this paper we review and introduce generalizations of the Golden ratio. We show that there exists a fundamental connection between the limit of two consecutive terms of recursive sequences, the generalized (p, q)-Golden ratio and the Golden ratio generated by the characteristic equation. We apply these generalizations to forecasting financial time series to illustrate one of their applications in signal processing.