Machine learning and Artificial Intelligence (AI) already support human decision-making complement professional roles, are expected in the future to be sufficiently trusted make autonomous decisions. To trust AI systems with such tasks, a high degree of confidence their behaviour is needed. However, can drastically different decisions if input data modified, way that would imperceptible humans....

The accurate forecasting of time series is difficult and for exchange rate more as well. Because it to predict they continuously fluctuate during trading hours. Exchange plays a vital financial problem in recent era. It extensively acknowledged that stability implies macroeconomic stability. In this study, hybrid model proposed forecast rates. Bayesian regularized neural network (BRNN) assemble...

The use of Artificial Neural Networks (ANN) has received increasing attention in the analysis and prediction of financial time series. Stationarity of the observed financial time series is the basic underlying assumption in the practical application of ANN on financial time series. In this paper, we will investigate whether it is feasible to relax the stationarity condition to non-stationary ti...

in this paper, we propose a new residual analysis method using fourier series transform into fuzzy time series model for improving the forecasting performance. this hybrid model takes advantage of the high predictable power of fuzzy time series model and fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...

In this paper, the gamma test is used to determine the order of lag-k tail dependence existing in financial time series. Using standardized return series, statistical evidence based on the test results show that jumps in returns are not transient. New time series models which combine a specific class of max-stable processes, Markov processes, and GARCH processes are proposed and used to model t...

Recently it was shown that financial time series are not completely random process but exhibit long-term or short-term dependences, which offer promises for predictability. However, we do not clearly understand the potential relationship between serial structure and predictability. This paper proposed a framework to magnify the correlations and regularities contained in financial time series th...

Modelling the dynamics of financial markets has been an area of active research in recent years. This paper presents a time series analysis model which can be used to infer patterns within financial data, in order to better understand the dynamics of financial markets. The focus of the paper is on finding causal and time-scale relationships between financial time series. Wavelets are used to ex...

An analysis of the stylized facts in financial time series is carried out. We find that, instead of the heavy tails in asset return distributions, the slow decay behaviour in autocorrelation functions of absolute returns is actually directly related to the degree of clustering of large fluctuations within the financial time series. We also introduce an index to quantitatively measure the cluste...