Sparse and short news headlines can be arbitrary, noisy, ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed accommodating long text to discover knowledge from them. Nonetheless, some of the existing research about text-based crude oil forecasting employs explore topics headlines, resulting in a mismatch between further affecting performance. Exploi...

Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect the global economy. Besides supply demand, prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, political events. Most previous research treats a time series or econometric variable prediction probl...

this paper, examines the factors that influence energy intensity- the ratio of energy consumption to gdp- in 42 oil exporting and none-oil exporting countries in the period 1985 to 2009. the research applies panel data models to analyze the impacts of oil price, gdp, exchange rate, co2 emissions, population, surface area and productivity on energy intensity. an attempt is made in this paper, to...

The difficulty in gas price forecasting has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forecasting gas prices however, all of the existing models of prediction cannot meet practical needs. In this paper, a novel hybrid intelligent framework is developed by applying a systematic integration of GMDH neural n...

This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is to create a benchmark based on lagged value of pre-processed spot price, the...