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...

We present a novel approach to probabilistic electricity price forecasting which utilizes distributional neural networks. The model structure is based on deep network containing so-called probability layer, i.e., the outputs of are parameters normal or Johnson’s SU distribution. To validate our approach, we conduct comprehensive study complemented by realistic trading simulation with day-ahead ...

Electricity Price Forecasting (EPF) influences the sale conditions in energy sector. Proper models of electricity price prognosis can be decisive for choice between sources as a start point transformation toward renewable sources. This article aims to present and compare various EPF scientific publications. Adopted this study procedure, publications are compared into two main categories: most p...

Timber prices belong to the most important variables affecting the optimality of forest management. On the other hand, forecasting of timber prices is very uncertain. One difficulty when using past time series data in forecasting future timber price development is the possibility of changes in the markets and in the society at large. Expert knowledge can be applied in forecasting of timber pric...

Modelling and forecasting the volatile spot pricing process for electricity presents a number of challenges. For increasingly deregulated electricity markets, like that in the Australian state of New South Wales, there is a need to forecast the price for a range of derivative securities used for hedging. Any forecasting model that hopes to capture the pricing dynamics within this market must be...

Predicting the price of a dynamic random access memory (DRAM) product is a critical task to the manufacturer. However, it is not easy to contend with the uncertainty of the price. In order to effectively predict the price of a DRAM product, an agent-based fuzzy collaborative intelligence approach is proposed in this study. In the agent-based fuzzy collaborative intelligence approach, each agent...