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The aim of this research is to analyze ANFIS performance for prediction of financial time series data. Financial time series data is usually characterized by volatility clustering, persistence, and leptokurtic data behavior. The financial time series data are usually non-stationary and non-linear. ARIMA has a good performance to predict linear time series(More)
Since its introduction by Molodstov (Computers & Mathematics with Applications 37(4):19–31 1999), soft set theory has been widely applied in various fields of study. Soft set theory has also been combined with other theories like fuzzy sets theory, rough sets theory, and probability theory. The combination of soft sets and probability theory generates(More)
  • Dedi Rosadi
  • 2016
Here we introduce some new linear dependence measures, namely the generalized covariation coefficient, generalized symmetric covariation coefficient and the generalized sign symmetric covariation coefficient. These measures can be applied for random variables which fulfill a certain linearity property and have finite first moments. Some basic mathematical(More)
It has widely known that the irreversible property of Markov chain representing the multirate multiservice loss system with Trunk Reservation policy is not satisfied. In this case, the well-known product form result for state probabilities cannot be applied. To obtain the exact state probabilities, one has to solve the balance equation of Markov chain that(More)
—Foreign exchange market is one of the most complex dynamic market with high volatility, non linear and irregularity. As the globalization spread to the world, exchange rates forecasting become more important and complicated. Many external factors influence its volatility. To forecast the exchange rates, those external variables can be used and usually(More)
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