Use of the Fractal Analysis of Non-stationary Time Series in Mobile Foreign Exchange Trading for M-Learning

Abstract

Mobile foreign exchange trading system for m-learning is proposed. It’s used for time series analysis skills learning. Method of pre-forecasting fractal R/S analysis of non-stationary time series is integrated in system. This method includes: persistence, anti-persistence and random level determination based on the calculation of the Hurst exponent. To calculate the average value of the nonperiodic cycle of time series, as well as to establish the potential profitable of assets that are represented by financial time series. A criterion for determination of the average length of non-periodic cycles based on the smoothing of V-statistics with simple moving average and Kaufman’s adaptive moving average is proposed. It has been confirmed that most financial time series are more or less persistent and endowed with long-term memory of their initial conditions using computer simulation. Time series of course pairs are close to random. Using fractal analysis in m-learning mobile foreign exchange trading systems for smartphones based on iOS or Android operating systems is suggested. The system is characterized by visualization and description of all stages, which has to be executed for time series analysis. Practical use of this system has shown high efficiency for time series analysis skills learning.