Title of the Paper: A Novel Minimax Probability Machine for Network Traffic Prediction
Authors: Mu Xiangyang , Zhang Taiyi
Abstract: Network traffic prediction is important to network planning, performance evaluation and network management directly. A variety of machine learning models such as artificial neural networks (ANN) and support vector machine (SVM) have been applied in traffic prediction. In this paper, a novel network traffic one-step-ahead prediction technique is proposed based on a state-of-the-art learning model called minimax probability machine (MPM). In the experiments, the predictive performance is tested on two different types of traffic data, Ethernet and MPEG4, at the same timescale. We find the predictions of MPM match the actual traffics accurately. Furthermore, we compare the MPM-based prediction technique to the SVM-based techniques. Results show that the predictive performance of MPM is competitive with SVM
Keywords: network traffic, minimax probability, support vector machine, prediction
Title of the Paper: Prediction of Financial Time Series with Time-Line Hidden
Markov Experts and ANNs
Authors: Georges Jabbour and Luciano Maldonado
Abstract: In this article, the use of Time-Line Hidden Markov Experts (THME) in the prediction of financial time series is presented and its efficiency is compared with that obtained using multilayer perceptron neural networks trained with BKP. The THME belongs to a focus known as mixture of experts, whose philosophy consists in decomposing the times series in states. Each expert models a particular state to achieve capturing the time series patterns in a sufficiently precise way, since for every situation in which time series can be found there is one or more experts that have the capacity to generate an adequate prognosis for the given situation. The state transition of each time series is time-variant. Experiments were carried out with 15 series of financial time series in which most of the world�s bursatile indexes can be found. The results show that THME models greatly surpass those of Artificial Neural Networks.
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