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This paper develops an integrated framework to forecast the volatility of crude oil prices by considering the impacts of extreme events (structural breaks). The impacts of extreme events are vital to improving prediction accuracy. Aiming to demonstrate the crude oil price fluctuation and the impacts of external events, this paper employs the complementary ensemble empirical mode decomposition (CEEMD). It decomposes the crude oil price into some constituents at various frequencies to extract a market fluctuation, a shock from extreme events and a long-term trend. The shock from extreme events is found to be the most crucial element in deciding the crude oil prices. Then we combine the iterative cumulative sum of squares (ICSS) test with the Chow test to get the structural breaks and analyze the extreme event impacts. Finally, this paper combines the structural breaks, the autoregressive integrated moving average (ARIMA) model, and the support vector machine (SVM) to make a forecast of the crude oil prices. The empirical process proves that the CEEMD-ARIMA-SVM model with structural breaks performs the best when compared with the other ARIMA-type models and SVM-type models. The framework offers an insightful view to help decision-makers and can be used in many areas. [ABSTRACT FROM AUTHOR]

In this paper, a novel data selection algorithm for identifying significant training data is presented for power quality (PQ) events classification. The key concept of this paper is to reduce the execution time, computational complexity and enhance the accuracy of the existing PQ classification system by reducing the number of support vectors. The proposed scheme identifies important training data and rejects redundant and irrelevant ones using a novel fuzzy C-means clustering-based data selection algorithm thereby reducing time of classification and enhancing accuracy. Significant features from raw PQ data are extracted using discrete wavelet transform and important training data are recognized using these features with the proposed data selection algorithm. Two machine learning algorithms namely the probabilistic neural network and support vector machine are employed and the best PQ classifier is investigated. Furthermore, the proposed data selection algorithm is integrated with the existing PQ classification system that has selected optimal input features and parameters of the classifier using Simulated Annealing and is shown to perform exceptionally well when compared to conventional classifiers that use full training data set. The suitability of the algorithm for noisy as well as real time data is examined and the empirical results show that the proposed scheme performs well compared to several existing PQ classification works. [ABSTRACT FROM AUTHOR]