The models considered so far in this book dealt with the case where the data can be modeled as realizations of a weakly dependent process. In this chapter, we consider a class of random processes that exhibit long-range dependence. The condition of long-range dependence in the data may be ...
Long-range-dependent time series are endemic in the statistical analysis of Internet traffic. The Hurst parameter provides a good summary of important self-similar scaling properties. We compare a number of different Hurst parameter estimation methods and some important variations. This is done in ...
On the predictability of long-range dependent series. Math. Probl. Eng. 2010, 2010, 397454. [CrossRef]M. Li and J. Y. Li, "On the predictability of long-range dependent series," Mathematical Problems in Engineering, vol. 2010, Article ID 397454, 2010....
Apositiverecurrent,aperiodicMarkovchainissaidtobelongrangedependent(LRD)when theindicatorfunctionofaparticularstateisLRD.Thishappensifandonlyifthereturntime distributionforthatstatehasinfinitevariance.Weinvestigatethequestionofwhetherother instantaneousfunctionsoftheMarkovchainalsoinheritthisproperty.Weprovideconditions ...
Impact of Long-Range Dependent Traffic in IoT Local Wireless Networks on Backhaul Link PerformanceIoT wireless networkBackhaul linkLong-range dependenceTraffic analysisPerformance evaluationPerformance evaluation in Internet of Things (IoT) networks is becoming more and more important due to the increasing ...
(2014) further generalize time-consistent (TC) MV portfolio optimization problems to incorporate state-dependent risk aversion. This framework has a wide range of applications, including TCMV longevity hedging (Wong et al., 2014), the TCMV reinsurance–investment problem under the constant elasticity...
long-range dependent and can be modeled as fractionally integrated processes, using, for example, long-memory stochastic volatility models. Are such long-range depen- dencies common among stocks? Are they caused by the same sources of variation?
it allows for dependent events, such as foreshocks and aftershocks, thus avoiding the tricky phase of catalog declustering. In addition, its simple, yet effective, parameterization makes our model suitable for large-scale applications. In this study, the model is coupled with a smoothed gridded ...
we notice that, while this paper confirms the persistence of LRD through a queue, the proposed solution is mainly theoretical, as the setting of the shaper parameter is critical and it must be finely tuned to the features of the first queue’s input traffic (which may be time-dependent). ...
3)Jiaxi Tang, Francois Belletti, Sagar Jain, Minmin Chen, Alex Beutel, Can Xu, Ed H. Chi: Towards Neural Mixture Recommender for Long Range Dependent User Sequences. WWW 2019: 1782-1793