Un Modèle FARIMA Localement Stationnaire

International audienceNous étudions le problème de la modélisation d'une série chronologique non stationnaire à longue mémoire au moyen d'un processus localement autorégressif à moyenne mobile fractionnairement intégrée. Le nombre de points de ruptures et leurs localisations sont inconnus ainsi que les paramètres de chaque sous-série. Nous présentons une méthode d'estimation des points de ruptures et des paramètres des sous-séries dont nous montrons les bonnes performances au moyen de simulations de Monte Carlo

Modeling non-stationary long-memory signals with large amounts of data

http://www.eurasip.org/Proceedings/Eusipco/Eusipco2011/papers/1569424681.pdfInternational audienceWe consider the problem of modeling long-memory signals using piecewise fractional autoregressive integrated moving average processes. The signals considered here can be segmented into stationary regimes separated by occasional structural break points. The number as well as the locations of the break points and the parameters of each regime are assumed to be unknown. An efficient estimation method which can manage large amounts of data is proposed. This method uses information criteria to select the number of structural breaks. Its effectiveness is illustrated by Monte Carlo simulations

Discovering Linear Models of Grid Workload

Despite extensive research focused on enabling QoS for grid users through economic and intelligent resource provisioning, no consensus has emerged on the most promising strategies. On top of intrinsically challenging problems, the complexity and size of data has so far drastically limited the number of comparative experiments. An alternative to experimenting on real, large, and complex data, is to look for well-founded and parsimonious representations. The goal of this paper is to answer a set of preliminary questions, which may help steering the design of those along feasible paths: is it possible to exhibit consistent models of the grid workload? If such models do exist, which classes of models are more appropriate, considering both simplicity and descriptive power? How can we actually discover such models? And finally, how can we assess the quality of these models on a statistically rigorous basis? Our main contributions are twofold. First we found that grid workload models can consistently be discovered from the real data, and that limiting the range of models to piecewise linear time series models is sufficiently powerful. Second, we presents a bootstrapping strategy for building more robust models from the limited samples at hand. This study is based on exhaustive information representative of a significant fraction of e-science computing activity in Europe

A procedure for modeling non-stationary signals with long range dependence

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac11-proceedings/data/html/papers/0270.pdfInternational audienceThe problem of modeling non-stationary signals with long range dependence is considered in this paper by using piecewise fractional autoregressive integrated moving average processes. In this piecewise model the number and the locations of structural change points as well as the parameters of each stationary regime are assumed to be unknown. We propose a procedure to find out all the parameters of the model. Its effectiveness is shown by Monte Carlo simulations and our method is applied to model Internet traffic data

A class of antipersistent processes

Abstract. We introduce a class of stationary processes characterized by the behaviour of their infinite moving average parameters. We establish the asymptotic behaviour of the covariance function and the behaviour around zero of the spectral density of these processes, showing their antipersistent character. Then, we discuss the existence of an infinite autoregressive representation for this family of processes, and we present some consequences for fractional autoregressive moving average models

Discovering Piecewise Linear Models of Grid Workload

International audienceDespite extensive research focused on enabling QoS for grid users through economic and intelligent resource provisioning, no consensus has emerged on the most promising strategies. On top of intrinsically challenging problems, the complexity and size of data has so far drastically limited the number of comparative experiments. An alternative to experimenting on real, large, and complex data, is to look for well-founded and parsimonious representations. This study is based on exhaustive information about the gLite-monitored jobs from the EGEE grid, representative of a significant fraction of e-science computing activity in Europe. Our main contributions are twofold. First we found that workload models for this grid can consistently be discovered from the real data, and that limiting the range of models to piecewise linear time series models is sufficiently powerful. Second, we present a bootstrapping strategy for building more robust models from the limited samples at hand

ARCH modeling in the presence of missing data

International audienc

Estimation of autoregressive models with epsilon-skew-normal innovations

International audienceWe consider the problem of modelling asymmetric near-Gaussian correlated signals by autoregressive models with epsilon-skew normal innovations. Moments and maximum likelihood estimators of the parameters are proposed and their limit distributions are derived. Monte Carlo simulation results are analyzed and the model is fitted to a real time series

Statistiques d'ordre superieur et modelisation en traitement du signal

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : T 81268 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc