Short term load forecasting is always an important study from operational and planning point of view. But short term price forecasting is a new topic. In this study, with the implementation of machine learning techniques, a new algorithm is proposed to predict both load and price values. A machine learning techniques such as Principle Component Analysis, and K nearest neighbor points, are appli...

This paper presents an hour-ahead System Marginal Price (SMP) forecasting framework based on a neural network. Recently, the deregulation in power industries has impacted on the power system operational problems. The bidding strategies in energy market among market participants are highly dependent on the short-term price levels. Therefore, short-term SMP forecasting is a very important issue t...

Palm oil is the most consumed and traded vegetable oils in the EU and the world. Increasing non-food uses for vegetable oils in especially feedstock of biofuels in recent years have caused the price volatility to rise in both EU and global market. The most efficient pricing of crude palm oil (CPO) is to found on Bursa Malaysia (BMD), and it provides by far the world’s most liquid palm oil contr...

This paper proposes a novel non-parametric approach for the analysis and prediction of electricity price curves by applying the manifold learning methodology. Cluster analysis based on the embedded low-dimensional manifold of the original price data is employed to identify characteristics of the price curve shape. The proposed price curve model performs well in forecasting both short-term price...

In this paper an adaptation of CO2RBFN, evolutionary COoperativeCOmpetitive algorithm for Radial Basis Function Networks design, applied to the prediction of the extra-virgin olive oil price is presented. In this algorithm each individual represents a neuron or Radial Basis Function and the population, the whole network. Individuals compite for survival but must cooperate to built the definite ...

a r t i c l e i n f o JEL classification: C52 C53 Q47 Keywords: Oil prices Futures markets Stochastic processes Kalman filter Forecasting Stochastic process models of commodity prices are important inputs in energy investment evaluation and planning problems. In this paper, we focus on modeling and forecasting the long-term price level, since it is the dominant factor in many such applications....

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock pr...