• Energy Research

Abstract Forecasting the future price of crude oil, which has an important role in the global economy, is considered as a hot matter for both investment companies and governments. However, forecasting the price of crude oil with high precision is indeed a challenging task because of the nonlinear dynamics of the crude oil time series, including chaotic behavior and inherent fractality. In this study, a new forecasting model based on support vector regression (SVR) with a wrapper-based feature selection approach using multi-objective optimization technique is developed to deal with this challenge. In our model, features based on technical indicators such as simple moving average (SMA), exponential moving average (EMA), and Kaufman’s adaptive moving average (KAMA) are utilized. SMA, EMA, and KAMA indicators are obtained from Brent crude oil closing prices under different parameters. The features based on SMA and EMA indicators are formed by changing the period values between 3 and 10. The features based on the KAMA indicator are obtained by changing the efficiency ratio (ER) period value, which is considered as fractality efficiency, between 3 and 10. The features are selected by the wrapper-based approach consisting of multi-objective particle swarm optimization (MOPSO) and radial basis function based SVR (RBFSVR) techniques considering both the mean absolute percentage error (MAPE) and Theil’s U values. The obtained empirical results show that the proposed forecasting model can capture the nonlinear properties of crude oil time series, and that better forecasting performance can be obtained in terms of precision and volatility than the other current forecasting models.

Abstract The price forecasting of the digital currencies in the financial market is of great importance, especially after the recent global economic crises. Due to the nonlinear dynamics, which is including inherent fractality and chaoticity of the digital currencies, it is understood from the research conducted by many researchers that a single model is not sufficient in forecasting the digital currencies with very high accuracy. Since the single models used in the forecasting of digital currencies have weaknesses as well as their own strengths, they might not grant the best forecasting achievement in all situations for all the time. A new hybrid-forecasting framework has been proposed in digital currency time-series to minimize this negative situation and increase forecasting achievement. In this study, a novel hybrid forecasting model based on long short-term memory (LSTM) neural network and empirical wavelet transform (EWT) decomposition along with cuckoo search (CS) algorithm is developed for digital currency time series. The model is obtained by combining the LSTM neural network and EWT decomposition technique, and optimizing the intrinsic mode function (IMF) estimated outputs with CS. The price of the four most traded digital currencies such as BTC, XRP, DASH and LTC, is estimated by the proposed model and the performance of the model has been tested. The experimental results show that the hybrid model proposed for digital currency forecasting can capture nonlinear properties of digital currency time series.

In this study, Bitcoin prediction is performed with Linear Regression (LR) and Support Vector Machine (SVM) from machine learning methods by using time series consisting of daily Bitcoin closing prices between 2012–2018. The prediction model with include the least error is obtained by testing with different parameter combinations such as SVM with including linear and polynomial kernel functions. Filters with different weight coefficients are used for different window lengths. For different window lengths, Bitcoin price prediction is made using filters with different weight coefficients. 10-fold cross-validation method in training phase is used in order to construct a model with high performance independent of the data set. The performance of the obtained model is measured by means of statistical indicators such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Pearson Correlation. It is seen that the price prediction performance of the proposed SVM model for Bitcoin data set is higher than that of the LR model.