Markov Process Research Papers - Academia.edu (original) (raw)

The paper describes studies of post-fire succession in heathland vegetation in N.E. Scotland, dominated by Calluna vulgaris. A preliminary model (Legg, 1978) suggested good agreement between simulation of succession on the basis ofa Markov chain and observations of stands at different stages of development after burning, at least in the earlier stages. Vegetation transitions are currently being recorded in permanent plots on burnt areas. First results confirm the view that (a) the post-fire succession has the properties ofa Markov process, (b) this type of model remains valid when constructed from records of actual transitions, rather than data obtained by inference from evidence of transition. Comparing successional events in stands where, at the time of burning, the Calluna population was in pioneer-, building-, mature-and degenerate phases, shows that transition matrices generally agree with the Markov hypothesis, but not in the case of stands where Calluna was degenerate when burnt. The composition of establishing vegetation ! year after fire is not confined to species normally associated with the early stages of succession, but reflects the composition of the stand before burning. Redevelopment after fire is described in terms of an initial floristic composition of species with strategies permitting early re-establishment, selected by the recurrence of the fire factor. Subsequent transitions represent changes in their relative abundance due to differing growth properties and competitive interactions. This interpretation applies only under conditions of recurrent incidence of fire (normally once in 10-15 yr). If fire does not recur, Calluna stands pass into the degenerate phase, where changes in the nature of relay floristics may come into play (e.g. with tree colonization).

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- Geography, Plant Ecology, Plant Biology, Ecology

An N-particle system with mean field interaction is considered.

- by Shui Feng
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- Statistics, Boolean Satisfiability, Markov Process

Obtaining accurate system models for verification is a hard and time consuming process, which is seen by industry as a hindrance to adopt otherwise powerful modeldriven development techniques and tools. In this paper we pursue an alternative approach where an accurate high-level model can be automatically constructed from observations of a given black-box embedded system. We adapt algorithms for learning finite probabilistic automata from observed system behaviors. We prove that in the limit of large sample sizes the learned model will be an accurate representation of the data-generating system. In particular, in the large sample limit, the learned model and the original system will define the same probabilities for linear temporal logic (LTL) properties. Thus, we can perform PLTL model-checking on the learned model to infer properties of the system. We perform experiments learning models from system observations at different levels of abstraction. The experimental results show the learned models provide very good approximations for relevant properties of the original system.

- by Brian Nielsen
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- Model Checking, Embedded Systems, Probabilistic Logic, Computer Model

Some time ago, the Markov processes were introduced in biomedical sciences in order to study disease history events. Homogeneous and Non-homogeneous Markov processes are an important field of research into stochastic processes, especially when exact transition times are unknown and interval-censored observations are present in the analysis. Non-homogeneous Markov process should be used when the homogeneous assumption is too strong. However these sorts of models increase the complexity of the analysis and standard software is limited. In this paper, some methods for fitting non-homogeneous Markov models are reviewed and an algorithm is proposed for biomedical data analysis. The method has been applied to analyse breast cancer data. Specific software for this purpose has been implemented.

- by Ricardo Ocaña-Riola
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- Algorithms, Statistics, Biomedical Engineering, Data Analysis

The main objective of this paper is to examine in some detail the dynamics and fluctuations in the critical situation for a simple model exhibiting bistable macroscopic behavior. The model under consideration is a dynamic model of a collection of anharmonic oscillators in a two-well potential together with an attractive mean-field interaction. The system is studied in the limit as the number of oscillators goes to infinity. The limit is described by a nonlinear partial differential equation and the existence of a phase transition for this limiting system is established. The main result deals with the fluctuations at the critical point in the limit as the number of oscillators goes to infinity. It is established that these fluctuations are non-Gaussian and occur at a time scale slower than the noncritical fluctuations. The method used is based on the perturbation theory for Markov processes developed by Papanicolaou, Stroock, and Varadhan adapted to the context of probability-measure-valued processes.

- by Donald Dawson
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- Statistical Physics, Mathematical Sciences, Physical sciences, Universality

We address a portfolio optimization problem in a semi-Markov modulated market. We study both the terminal expected utility optimization on finite time horizon and the risk-sensitive portfolio optimization on finite and infinite time horizon. We obtain optimal portfolios in relevant cases. A numerical procedure is also developed to compute the optimal expected terminal utility for finite horizon problem.

- by Anindya Goswami
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- Mathematics, Applied Mathematics, Fixed Income Securities, Portfolio Optimization

This paper surveys the current research literature on the stochastic lot scheduling problem which deals with scheduling production of multiple products with random demand on a single facility with limited production capacity and signi"cant change-overs between products. The deterministic version of this problem has received signi"cant coverage in the literature; however, the stochastic problem has been addressed only recently. Furthermore, a range of distinctly di!erent analytical methods have been applied to this problem. This paper provides a unifying framework for discussing these approaches and o!ers some explanation and clari"cation of the di!erent analytical methods for this problem. After discussing some of the modeling and managerial implications of this problem, a detailed review of both continuous and discrete time control strategies is given, and areas for further research are outlined.

- by Alan Bowman
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- Modeling, Scheduling, Multidisciplinary, National Science Foundation

The evolution of an infectious disease outbreak in an isolated population is split into two stages: a stochastic Markov process describing the initial contamination and a linked deterministic dynamical system with random initial conditions for the continued development of the outbreak. The initial contamination stage is well approximated by the randomized SI (susceptible/infected) model. We obtain the probability density function for the early behavior of the epidemic. This provides an appropriate distribution for the initial conditions with which to describe the subsequent deterministic evolution of the system. We apply the method of matching asymptotic expansions to link the two stages. This allows us to estimate the standard deviation of the number of infectives in the developed outbreak, and the statistical characteristics of the outbreak time. The potential trajectories caused by the stochastic nature of the contamination stage show greatest divergence at the initial and fade-out stages and coincide most tightly just after the peak of the epidemic. The time to the peak of the outbreak is not strongly dependent on the initial trajectory.

Background: The global resurgence of tuberculosis is a significant threat. Lamiaceae members have been used in folk remedies for centuries. This study was designed to assess the in-vitro antimycobacterial activity of eighteen crude extracts from six plants (Lamiaceae) and to characterize their phenolic and flavonoid compounds. Methods: Six Turkish medicinal plants of the family Lamiaceae (Stachys tmolea Boiss., Stachys thirkei C. Koch, Ballota acetabulosa (L.) Benth., Thymus sipthorpii Benth., Satureja aintabensis P.H. Davis, and Micromeria juliana (L.) Benth. ex Reich.) were collected in 2009-2010. Dried and crushed plant samples were subjected to sequential extraction with petroleum ether, ethyl acetate, and methanol in order of increasing polarity. A broth microdilution method was employed to screen extracts against four mycobacterial strains of Mycobacterium tuberculosis. Phenolic and flavonoid compounds were characterized by liquid chromatography-mass spectrometry. Results: S. aintabensis, T. sibthorpii, and M. juliana were found to develop considerable activity against the four strains of M. tuberculosis with the minimal inhibitory concentrations value of 12.5-100 μg/ml. S. aintabensis and T. sibthorpii extracts killed M. tuberculosis with the minimum bactericidal concentration value of 50-800 μg/ml. On the basis of these prominent antimycobacterial activity, we suggest that they could be a source of natural anti-tuberculosis agents. Conclusion: S. aintabensis and T. sibthorpii showed activity by killing Mycobacteria strains. The major phenolic compound was rosmarinic for T. sibthorpii and S. aintabensis. Flavonoids might be "a modal" for the drug design.

- by Timo Laurmaa
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- Computer Science, Performance Evaluation, Protocol, Acm

In this paper, we consider a system whose state x changes to if a perturbation occurs at the time t, for and the state x changes to the new state at the time t, for . Here, and are logistic maps. We assume that the number of perturbations... more

- by GAMALIEL BLE
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- Mathematics, Applied Mathematics, Pure Mathematics, Invariant Measure

Limited battery power at wireless video sensor nodes, along with the transmission quality requirements for video data, makes quality-of-service (QoS) provisioning in a wireless video sensor network a very challenging task. In this paper, a dynamic power-management framework is proposed for a wireless video sensor node to improve the energy-saving performance while satisfying video transmission quality requirements. This framework is developed based on a Markov decision process that considers the video traffic arrival process in the sensor node, the sleep and wakeup processes in the camera and wireless transceiver electronics, the queue status, and the wireless channel condition. A dynamic programming approach is used to find the optimum policy to achieve the desired performance measures in an energylimited sensor node.

- by Afshin Fallahi
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- Engineering, Technology, Remote Sensing, Service Quality
- by Emile Hendriks
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- Information Systems, Algorithms, Artificial Intelligence, Sign Language

The problem of predicting a future value of a time series is considered in this paper. If the series follows a stationary Markov process, this can be done by nonparametric estimation of the autoregression function. Two forecasting algorithms are introduced. They only differ in the nonparametric kernel-type estimator used: the Nadaraya-Watson estimator and the local linear estimator. There are three major issues in the implementation of these algorithms: selection of the autoregressor variables; smoothing parameter selection and computing prediction intervals. These have been tackled using recent techniques borrowed from the nonparametric regression estimation literature under dependence. The performance of these nonparametric algorithms has been studied by applying them to a collection of 43 well-known time series. Their results have been compared to those obtained using classical Box-Jenkins methods. Finally, the practical behaviour of the methods is also illustrated by a detailed analysis of two data sets.

- by Ricardo Cao
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- Data Analysis, Time Series, Prediction, Comparative Study

In this paper we give an essentially self-contained account of some general structural properties of the dynamics of quantum open Markovian systems. We review some recent results regarding the problem of the classification of quantum Markovian master equations and the limiting conditions under which the dynamical evolution of a quantum open system obeys an exact semigroup law (weak coupling limit and singular coupling limit). We discuss a general form of quantum detailed balance and its relation to thermal relaxation and to microreversibility. * This paper is an extended version of a lecture delivered by V. Gorini at the 8th Symposium on Mathematical Physics, Tort@ Poland, December 3-6, 1975.

- by Maurizio Verri
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- Stochastic Process, Kinetics, Quantum Theory, Quantum Mechanics

The aim of this paper is to present an R library, called tdc.msm, developed to analyze multi-state survival data. In this library, the time-dependent regression model and multi-state models are included as two possible approaches for such data. For the multi-state modelling five different models are considered, allowing the user to choose between Markov and semi-Markov property, as well as

- by Carmen Cadarso-suárez
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- Biomedical Engineering, Survival Analysis, Spain, Software

Fractal properties of surfaces have been explored by many investigators. Most have concluded that fractal characterisation is useful. This note questions the philosophy of using fractals to describe and control engineering surfaces. It concludes that the benefits are more virtual than real. The functional significance of fractal parameters is also examined and the overall question arises as to whether scale invariant parameters are appropriate.

We introduce a general class of interest rate models in which the value of pure discount bonds can be expressed as a functional of some (lowdimensional) Markov process. At the abstract level this class includes all current models of practical importance. By specifying these models in Markov-functional form, we obtain a specification which is efficient to implement. An additional advantage of Markov-functional models is the fact that the specification of the model can be such that the forward rate distribution implied by market option prices can be fitted exactly, which makes these models particularly suited for derivatives pricing. We give examples of Markov-functional models that are fitted to market prices of caps/floors and swaptions.

- by Antoon Pelsser and +1
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- Applied Mathematics, Statistics, Derivative Pricing, Option pricing

In this paper we use a stochastic programming approach to develop currency option hedging models which can address problems with multiple random factors in an imperfect market. The portfolios considered in our model are rebalanced at the end of each time period, and reinvestments are allowed during the hedging process. These sequential decisions (reinvestments) are based on the evolution of random parameters such as exchange rates, interest rates, etc. We also allow the inclusion of a variety of instruments in the hedging portfolio, including short term derivative securities, short term options, and futures. These instruments help generate strategies that provide good liquidity and low trade intensity. One of the important features of the model is that it incorporates constraints on sensitivity measures such as Delta and Gamma. By ensuring that these hedge parameters track a desired trajectory (e.g., the parameters of a target option), the new model provides investment strategies that are robust with respect to the perturbations measured by Delta and Gamma. In order to manage the explosion of scenarios due to multiple random factors, we incorporate sampling within a scenario aggregation algorithm. We illustrate that when compared with other myopic hedging methods in imperfect markets, the new stochastic programming model can provide better performance. Our examples also illustrate stochastic programming as a practical computational tool for realistic hedging problems.

In this paper, we provide a framework based upon diffusion processes for finding meaningful geometric descriptions of data sets. We show that eigenfunctions of Markov matrices can be used to construct coordinates called diffusion maps that generate efficient representations of complex geometric structures. The associated family of diffusion distances, obtained by iterating the Markov matrix, defines multiscale geometries that prove to be useful in the context of data parametrization and dimensionality reduction. The proposed framework relates the spectral properties of Markov processes to their geometric counterparts and it unifies ideas arising in a variety of contexts such as machine learning, spectral graph theory and eigenmap methods.

- by Ronald Coifman
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- Applied Mathematics, Machine Learning, Pure Mathematics, Manifold learning
- by Derek Abbott
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- Game Theory, Decision Theory, Mathematical Analysis, Brownian Motion

Microsoft is planning the introduction of Internet Explorer along with Windows 95. Issues include how aggressive the company should be in providing its browser with Windows 95 and restricting OEMs (original-equipment manufacturers) from putting other browsers on their computers. Should Microsoft go for initial share, concentrate on stealing over time, retain customers, or enlarge the total size of the browser market? Students use a Markov process with initial states and switching probabilities to gain insight into resolving these issues.

- by Samuel Bodily
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- Markov Process

an EM algorithm for obtaining maximum likelihood estimates of parameters for processes subject to discrete shifts in autoregressive parameters, with the shifts themselves modeled as the outcome of a discrete-valued Markov process. The... more

- by James D. Hamilton
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- Econometrics, Time Series, Parameter estimation, Maximum Likelihood

Monte Carlo Tree Search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarise the results from the key game and non-game domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.

- by Diego Perez
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- Game Theory, Decision Theory, Artificial Intelligent, Random sampling

A model for network panel data is discussed, based on the assumption that the observed data are discrete observations of a continuous-time Markov process on the space of all directed graphs on a given node set, in which changes in tie variables are independent conditional on the current graph. The model for tie changes is parametric and designed for applications to social network analysis, where the network dynamics can be interpreted as being generated by choices made by the social actors represented by the nodes of the graph. An algorithm for calculating the Maximum Likelihood estimator is presented, based on data augmentation and stochastic approximation. An application to an evolving friendship network is given and a small simulation study is presented which suggests that for small data sets the Maximum Likelihood estimator is more efficient than the earlier proposed Method of Moments estimator.

- by Johan Koskinen
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- Marketing, Stochastic Process, Statistics, Method of Moments

We introduce the concept of a Markov risk measure and we use it to formulate risk-averse control problems for two Markov decision models: a finite horizon model and a discounted infinite horizon model. For both models we derive risk-averse dynamic programming equations and a value iteration method. For the infinite horizon problem we develop a risk-averse policy iteration method and we prove its convergence. We also propose a version of the Newton method to solve a nonsmooth equation arising in the policy iteration method and we prove its global convergence. Finally, we discuss relations to min-max Markov decision models.

- by Andrzej Ruszczynski
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- Applied Mathematics, Optimal Control, Mathematical Programming, Modeling
- by Arne Hallam
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- Stochastic Process, Econometrics, Statistics, Econometric Theory
- by James Baker
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- Bit Error Rate, Markov Process

We consider the asymptotic behavior of posterior distributions and Bayes estimators based on observations which are required to be neither independent nor identically distributed. We give general results on the rate of convergence of the posterior measure relative to distances derived from a testing criterion. We then specialize our results to independent, nonidentically distributed observations, Markov processes, stationary Gaussian time series and the white noise model. We apply our general results to several examples of infinite-dimensional statistical models including nonparametric regression with normal errors, binary regression, Poisson regression, an interval censoring model, Whittle estimation of the spectral density of a time series and a nonlinear autoregressive model. POSTERIOR CONVERGENCE RATES 193 dressed by Amewou-Atisso, Ghosal, Ghosh and Ramamoorthi [1] and Choudhuri, Ghosal and Roy . The main purpose of the present paper is to obtain a theorem on rates of convergence of posterior distributions in a general framework not restricted to the setup of i.i.d. observations. We specialize this theorem to several classes of non-i.i.d. models including i.n.i.d. observations, Gaussian time series, Markov processes and the white noise model. The theorem applies in every situation where it is possible to test the true parameter versus balls of alternatives with exponential error probabilities and it is not restricted to any particular structure on the joint distribution. The existence of such tests has been proven in many special cases by Le Cam and Birgé , who used them to construct estimators with optimal rates of convergence, determined by the (local) metric entropy or "Le Cam dimension" of the model. Our main theorem uses the same metric entropy measure of the complexity of the model and combines this with a measure of prior concentration around the true parameter to obtain a bound on the posterior rate of convergence, generalizing the corresponding result of Ghosal, Ghosh and van der Vaart . We apply these results to obtain posterior convergence rates for linear regression, nonparametric regression, binary regression, Poisson regression, interval censoring, spectral density estimation and nonlinear autoregression. van der Meulen, van der Vaart and van Zanten have extended the approach of this paper to several types of diffusion models.

- by rk choudhury
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- Stochastic Process, Econometrics, Statistics, Time Series

The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and... more

- by Andre Santos
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- Mathematics, Mathematical Statistics, Stochastic Process, Calculus

The authors review research on judgments of random and nonrandom sequences involving binary events with a focus on studies documenting gambler's fallacy and hot hand beliefs. The domains of judgment include random devices, births, lotteries, sports performances, stock prices, and others. After discussing existing theories of sequence judgments, the authors conclude that in many everyday settings people have naive complex models of the mechanisms they believe generate observed events, and they rely on these models for explanations, predictions, and other inferences about event sequences. The authors next introduce an explanation-based, mental models framework for describing people's beliefs about binary sequences, based on 4 perceived characteristics of the sequence generator: randomness, intentionality, control, and goal complexity. Furthermore, they propose a Markov process framework as a useful theoretical notation for the description of mental models and for the analysis of actual event sequences.

- by Gary McClelland
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- Marketing, Psychology, Cognitive Science, Culture

We consider a stochastic susceptible-exposed-infected-recovered ͑SEIR͒ epidemiological model. Through the use of a normal form coordinate transform, we are able to analytically derive the stochastic center manifold along with the associated, reduced set of stochastic evolution equations. The transformation correctly projects both the dynamics and the noise onto the center manifold. Therefore, the solution of this reduced stochastic dynamical system yields excellent agreement, both in amplitude and phase, with the solution of the original stochastic system for a temporal scale that is orders of magnitude longer than the typical relaxation time. This new method allows for improved time series prediction of the number of infectious cases when modeling the spread of disease in a population. Numerical solutions of the fluctuations of the SEIR model are considered in the infinite population limit using a Langevin equation approach, as well as in a finite population simulated as a Markov process.

- by Lora Billings
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- Applied Mathematics, Stochastic Process, Nonlinear dynamics, Pattern Formation

A formula for the transition density of a Markov process defined by an infinite-dimensional stochastic equation is given in terms of the Ornstein–Uhlenbeck bridge and a useful lower estimate on the density is provided. As a consequence, uniform exponential ergodicity and V-ergodicity are proved for a large class of equations. We also provide computable bounds on the convergence rates and the spectral gap for the Markov semigroups defined by the equations. The bounds turn out to be uniform with respect to a large family of nonlinear drift coefficients. Examples of finite-dimensional stochastic equations and semilinear parabolic equations are given.

- by B. Goldys and +1
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- Statistics, Markov Process

In general, for multitarget problems where the number of targets and their states are time-varying, the optimal Bayesian multitarget tracking is computationally demanding. The Probability Hypothesis Density (PHD) filter, which is the first-order moment approximation of the optimal one, is a computationally tractable alternative. By evaluating the PHD, the number of targets as well as their individual states can be extracted. Recent Sequential Monte Carlo (SMC) implementations of the PHD filter have paved the way to its application to realistic nonlinear non-Gaussian problems. It is observed that the particle implementation of the PHD filter is dependent on current measurements, especially in the case of low observable target problems (i.e., estimates are sensitive to missed detections and false alarms). In this paper, a PHD smoothing algorithm is proposed to improve the capability of PHD-based tracking system. It involves forward multitarget filtering using the standard PHD filter recursion followed by backward smoothing recursion using a novel recursive formula. Smoothing, which produces delayed estimates, results in better estimates for target states and a better estimate for the number of targets. Multiple Model PHD (MMPHD) smoothing, which is an extension of the proposed technique to maneuvering targets, is also provided. Simulations are performed with the proposed method on a multitarget scenario. Simulation results confirm improved performance of the proposed algorithm.

- by Nandakumaran Nadarajah and +1
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- Aerospace Engineering, Approximation Theory, Markov Processes, Aerospace

Motivated by the development of efficient Monte Carlo methods for PDE models in molecular dynamics, we establish a new probabilistic interpretation of a family of divergence form operators with discontinuous coefficients at the interface of two open subsets of R d . This family of operators includes the case of the linearized Poisson-Boltzmann equation used to compute the electrostatic free energy of a molecule.

- by Nicolas Champagnat and +1
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- Applied Mathematics, Numerical Analysis, Algorithm, Numerical Method

The purpose of this tutorial is to survey queueing networks, a class of stochastic models extensively applied to represent and analyze resource sharing systems such as communication and computer systems. Queueing networks (QNs) have been proved to be a powerful and versatile tool for system performance evaluation and prediction. First we briefly survey QNs that consist of a single service center, i.e., the basic queueing systems defined under various hypotheses, and we discuss their analysis to evaluate a set of performance indices, such as resource utilization and throughput and customer response time. Their solution is based on the introduction of an underlying stochastic Markov process. Then, we introduce QNs that consist of a set of service centers representing the system resources that provide service to a collection of customers that represent the users. Various types of customers define the customers classes in the network that are gathered in chains. We consider various analytical methods to analyze QNs with single-class and multiple-class. We mostly focus on product-form QNs that have a simple closed form expression of the stationary state distribution that allows to define efficient algorithms to evaluate average performance measures. We review the basic results, stating from the BCMP theorem that defines a large class of product-form QNs, and we present the main solution algorithms for single-class e multiple-class QNs. We discuss some interesting properties of QNs including the arrival theorem, exact aggregation and insensitivity. Finally, we discuss some particular models of product-form QNs that allow to represent special system features such as state-dependent routing, negative customers, customers batch arrivals and departures and finite capacity queues. The class of QN models is illustrated through some application examples of to analyze computer and communication systems.

We implement a model for the estimation of expected bond returns (EBR) based on a discrete time Markov process of rating transition. We use US corporate bond transaction data and a rating agency transition matrix to extract the term structure of EBR. We describe some early results of the properties of this EBR model and outline our plans to explore

- by Zvika Afik
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- Credit Risk, Markov Process, Corporate Bonds, Term Structure

This paper presents a new physics-based statistical model for random telegraph noise in Flash memories. From the probabilistic superposition of elementary Markov processes describing the trapping/detrapping events taking place in the cell tunnel oxide, the model can explain the main features of the random telegraph noise threshold-voltage instability. The results on the statistical distribution of the threshold-voltage difference between two

- by Riccardo Gusmeroli
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- Flash memory, Statistical Model, New Physics, Electron Devices

We propose a scheme for quantum Brownian motion of a particle in a fermionic bath. Based on the spin coherent-state representation of the noise operators and a canonical thermal distribution of the associated c numbers, we derive a quantum analog of generalized Langevin equation for quantum-mechanical mean position of the particle subjected to an external force field. The approach allows us to map the quantum problem on a classical setting. The quantum dispersion around the mean can be estimated order by order by a set of quantum correction equations up to a desired degree of accuracy for a given nonlinear potential. We derive a quantum diffusion equation for free particle and show that quantization, in general, enhances the mean-square displacement. Increase in temperature leads to suppression of mean-square displacement. The method is based on canonical quantization procedure and may be used for understanding diffusive transport and thermally activated processes in a fermionic bath.

- by debasish deb
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- Engineering, Quantum Mechanics, Quantum Transport, Mathematical Sciences

This paper proposes a stochastic wind power model based on an autoregressive integrated moving average (ARIMA) process. The model takes into account the nonstationarity and physical limits of stochastic wind power generation. The model is constructed based on wind power measurement of one year from the Nysted offshore wind farm in Denmark. The proposed limited-ARIMA (LARIMA) model introduces a limiter and characterizes the stochastic wind power generation by mean level, temporal correlation and driving noise. The model is validated against the measurement in terms of temporal correlation and probability distribution. The LARIMA model outperforms a first-order transition matrix based discrete Markov model in terms of temporal correlation, probability distribution and model parameter number. The proposed LARIMA model is further extended to include the monthly variation of the stochastic wind power generation. , he was also with ABB Corporate Research, Sweden. He is currently pursuing his Ph.D. degree at Aalborg University, Denmark.