With the increase in popularity of cryptocurrencies, it is becoming extremely crucial to predict what the prices of the currencies are going to be in the future. This paper uses a dataset that consists of over 1500 cryptocurrencies with their prices starting from their initiation till May, 2018....

Reliable forecast of energy demand represents a starting point in policy development and improvement of production and distribution facilities. This paper predicts the electricity demand in Sudan during the period from January 2006 to December 2016. For the purposes of these forecasts,...

Stock market is basically volatile and the prediction of its movement will be more useful to the stock traders to design their trading strategies. An intelligent forecasting will certainly abet to yield significant profits. Many important models have been proposed in the economics and finance...

Conventionally, ARIMA (Autoregressive Integrated Moving Average) has been one of the most accepted, used and fundamental model in field of time series analysis and prediction. However, the ARIMA is a parametric model that cannot easily capture the nonlinear nature. In spite of these...

Volatility had been used as an indirect means for predicting risk accompanied with the asset. Volatility explains the variations in returns. Forecasting volatility had been a stimulating problem in the financial systems. The study examined the different volatility estimators and determined the...

We introduce a novel approach to multifractal data in order to achieve transcended modeling and forecasting performances by extracting time series out of local Hurst exponent calculations at a specified scale. First, the long range and co-movement dependencies of the time series are scrutinized...