Variable bit rate video traffic modeling by multiplicative multifractal model

Multiplicative multifractal process could well modal video traffic. The multiplier distributions in the multiplicatire multifractal model for video traffic are investigated and it is found that Gaussian is not suitable for describing the multipliers on the small time scales. A new statistical distribution-symmetric Pareto distribution is introduced. It is applied instead of Gaussian for the multipliers on those scales. Based on that, the algorithm is updated so that symmetric pareto distribution and Gaussian distribution are used to model video traffic but on different time scales. The simulation results demonstrate that the algorithm could model video traffic more accurately.

A Multifractal Detrended Fluctuation Analysis of the Ising Financial Markets Model with Small World Topology

We present a multifractal detrended fluctuation analysis （MFDFA） of the time series of return generated by our recently-proposed Ising financial market model with underlying small world topology. The result of the MFDFA shows that there exists obvious multifractal scaling behavior in produced time series. We compare the MFDFA results for original time series with those for shuffled series, and find that its multifractal nature is due to two factors： broadness of probability density function of the series and different correlations in small- and large-scale fluctuations. This may provide new insight to the problem of the origin of multifractality in financial time series.

Improved multi-fractal network traffic model and its performance analysis

This paper demonstrates the limitation of the traditional multi-fractal wavelet model （MWM）. Through analyzing the multi-resolution behaviors of the real video traffic, we propose an improved MWM model. It synthesizes the traffic traces using another wavelet basis, and can adjust wavelet coefficients and multiplicative coefficients at each time scale, based on the network measurement. Subsequently, multifractal spectra and queue performances of the new model have been analyzed. The simulation proves it can capture the multifractal behaviors of network traces.

Exo-LSTM: traffic flow prediction based on multifractal wavelet theory

In order to predict traffic flow more accurately and improve network performance, based on the multifractal wavelet theory, a new traffic prediction model named exo-LSTM is proposed. Exo represents exogenous sequence used to provide a detailed sequence for the model, LSTM represents long short-term memory used to predict unstable traffic flow. Applying multifractal traffic flow to the exo-LSTM model and other existing models, the experiment result proves that exo-LSTM prediction model achieves better prediction accuracy.

Mineral Exploration Using Subtle or Negative Geochemical Anomalies

Mineral resources prediction and assessment is one of the most important tasks in geosciences.Geochemical anomalies,as direct indicators of the presence of mineralization,have played a significant role in the search of mineral deposits in the past several decades.In the near future,it may be possible to recognize subtle geochemical anomalies through the use of processing of geochemical exploration data using advanced approaches such as the spectrum-area multifractal model.In addition,negative geochemical anomalies can be used to locate mineralization.However,compared to positive geochemical anomalies,there has been limited research on negative geochemical anomalies in geochemical prospecting.In this study,two case studies are presented to demonstrate the identification of subtle geochemical anomalies and the significance of negative geochemical anomalies.Meanwhile,the opportunities and challenges in evaluating subtle geochemical anomalies associated with mineralization,and benefits of mapping of negative anomalies are discussed.