Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autor...

This paper examines the forward pricing efficiency of the local crude palm oil (CPO) futures market. In an efficient market, the relevant signal to be used by -the producers, traders and processors is simply the futures price. The forward pricing efficiency is measured in terms of the forecasting ability of Malaysian crude palm oil futures price on physical price. The relative predictive power ...

The presence of stock market efficiency is a distinctive characteristic of the effectively functioning market economy. Investigation of the market efficiency of seven emerging East-European stock exchanges is carried out as their major stock indices (BELEX15, BET, CROBEX, ISE100, PFTS, RTSI, SOFIX) are studied in respect of long-range dependence (LRD), persistency, and forecasting possibili...

Classifier ensemble techniques are effectively used to combine the responses provided by a set of classifiers. Classifier ensembles improve the performance of single classifier systems, even if a large number of classifiers is often required. This implies large memory requirements and slow speeds of classification, making their use critical in some applications. This problem can be reduced by s...

In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We present an adaptation of several popular ensemble techniques in machine learnin...

Trust is a major issue with deploying empirical models in the real world since changes in the underlying system or use of the model in new regions of parameter space can produce (potentially dangerous) incorrect predictions. The trepidation involved with model usage can be mitigated by assembling ensembles of diverse models and using their consensus as a trust metric, since these models will be...

In this work, we propose the use of support vector regression ensembles for wind power prediction. Ensemble methods often yield better classification and regression accuracy than classical machine learning algorithms and reduce the computational cost. In the field of wind power generation, the integration into the smart grid is only possible with a precise forecast computed in a reasonable time...

The existing techniques of forecasting a future count either treat the time series of counts as a Gaussian time series or use a random effects based dynamic Poisson model. The normality based approach may not yield valid forecasting, whereas the random effects based model usually generates a complex correlation structure for the time series of counts which may be impractical to use for forecast...

A campus based smart micro grid model that combined power and heat is presented. In this study, optimal scheduling of distributed generations inside these smart micro grids is determined based on demand forecasting, solar power forecasting and wind power forecasting by artificial neural network (ANN). The proposed model is then evaluated through economical efficiency, environment efficiency and...