Short-range ocean forecast and reanalysis systems routinely combine observations from satellite altimetry, satellite sea surface temperature (SST), and in situ temperature and salinity, to initialise global and regional ocean models. The most critical observation type for eddy-resolving applications is arguably satellite altimetry. To quantify the impact of satellite altimetry observations on a...

We consider an e-business web-server system where the network traffic exhibits self-similarity. We demonstrate that traditional techniques are unsuitable for predicting the network performance under such traffic conditions. Instead, we propose and demonstrate a novel decomposition approximation technique that helps predict delays more accurately and thus is better suited for capacity planning a...

The notion of long range dependence has traditionally been deened through a slow decay of correlations. This approach may be completely inappropriate in the case of a stochastic process with heavy tails. Yet long memory has been reported to be found in various elds where heavy tails are a standard feature of the commonly used stochastic models. Financial and communications networks data are amo...

There has been a growing concern about the potential impact of long-term correlations (second-order statistic) in variable-bit-rate (VBR) video traac on ATM buuer dimen-sioning. Previous studies have shown that video traac exhibits long-range dependence (LRD) (Hurst parameter large than 0.5). We investigate the practical implications of LRD in the context of realistic ATM traac engineering by s...

Over the last few years, the network community has started to make heavy use of novel concepts such as self-similarity and Long-Range Dependence (LRD). Despite their wide use, there is still much confusion regarding the identification of such phenomena in real network traffic data. In this paper, we show that estimating Long-Range Dependence is not straightforward: there is no systematic or def...

Analysis and modeling of computer network traffic is a daunting task considering the amount of available data. This is quite obvious when considering the spatial dimension of the problem, since the number of interacting computers, gateways and switches can easily reach several thousands, even in a Local Area Network (LAN) setting. This is also true for the time dimension: W. Willinger and V. Pa...

Multivariate processes with long-range dependent properties are found in a large number of applications including finance, geophysics and neuroscience. For real data applications, the correlation between time series is crucial. Usual estimations of correlation can be highly biased due to phase-shifts caused by the differences in the properties of autocorrelation in the processes. To address thi...

Detection of a long-range time dependence in the radial cross-correlation function is normally difficult because of the oscillatory behavior of the cross-correlation tail, its low level of coherence, and noise contamination. This problem persists, even with large statistical samples. In this paper, a method for investigating long-range dependence in a single time series is extended to the calcu...

A simple first-order recurrence in a (max, +) dynamic system is numerically investigated and shown to exhibit statistical long-range dependence, characterized by slowly decaying aggregated variances and power-law evolutions of the autocorrelation and spectrum. We propose this model as a basis for a very parsimonious modeling of some long-range dependent processes such as data traffic.

This paper presents empirical evidence of long range dependence in returns and volatility for banking indices for 41 different countries. We employ the Rescaled Hurst analysis and develop a formal statistical procedure to test for long range dependence. This procedure allows to rank these countries by relative inefficiency, which can provide guidance for investors and portfolio managers. Keywor...