Markov-switching autoregressive models for wind time series

Ailliot, Pierre; Monbet, Valérie

doi:10.1016/j.envsoft.2011.10.011

Cited by 103 publications

(98 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, the ability of a model to duplicate the length of stormy and calm periods is investigated in [36]. For each of the simulation scenarios outlined in Section 3, we are interested in replicating the following features of the observed data:…”

Section: Validationmentioning

confidence: 99%

“…Most models focus primarily on simulating wind speed or wind power for one temporal sampling frequency, with a few exceptions that model the wind vector [29,32,35]. Some more advanced approaches that use Markov-switching autoregressive [36] or vector autoregressive models have been proposed [20] with the underlying assumption that many locations tend to observe one of a few prevailing wind regimes, and characteristics within these regimes may differ dramatically [37,38].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales

2015

View full text Add to dashboard Cite

Despite recent efforts to record wind at finer spatial and temporal scales, stochastic realizations of wind are still important for many purposes and particularly for wind energy grid integration and reliability studies. Most instances of wind generation in the literature focus on simulating only wind speed, or power, or only the wind vector at a particular location and sampling frequency. In this work, we introduce a Markov-switching vector autoregressive (MSVAR) model, and we demonstrate its flexibility in simulating wind vectors for 10-min, hourly and daily time series and for individual, locally-averaged and regionally-averaged time series. In addition, we demonstrate how the model can be used to simulate wind vectors at multiple locations simultaneously for an hourly time step. The parameter estimation and simulation algorithm are presented along with a validation of the important statistical properties of each simulation scenario. We find the MSVAR to be very flexible in characterizing a wide range of properties in the wind vector, and we conclude with a discussion of extensions of this model and modeling choices that may be investigated for further improvements.

show abstract

Section: Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales

2015

View full text Add to dashboard Cite

show abstract

“…In fact, the autoregressive assumption in HMM has shown its advantage over regular HMM that cannot catch the strong dependence between successive observations (e.g., Ref. [9]). A similar model to ours can be found in Ref.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Multivariate Autoregressive Hidden Markov Model with Application to Breast Cancer Biomarker Modeling

Maroufy¹,

Hibbah²,

Zyad³

et al. 2017

Bayesian Inference

View full text Add to dashboard Cite

In this work, a first-order autoregressive hidden Markov model (AR(1)HMM) is proposed. It is one of the suitable models to characterize a marker of breast cancer disease progression essentially the progression that follows from a reaction to a treatment or caused by natural developments. The model supposes we have observations that increase or decrease with relation to a hidden phenomenon. We would like to discover if the information about those observations can let us learn about the progression of the phenomenon and permit us to evaluate the transition between its states (supposed discrete here). The hidden states governed by the Markovian process would be the disease stages, and the marker observations would be the depending observations. The parameters of the autoregressive model are selected at the first level according to a Markov process, and at the second level, the next observation is generated from a standard autoregressive model of first order (unlike other models considering the successive observations are independents). A Markov Chain Monte Carlo (MCMC) method is used for the parameter estimation, where we develop the posterior density for each parameter and we use a joint estimation of the hidden states or block update of the states.

show abstract

“…Thanks to their ability to fit time series subject to changes in pattern, these models have entertained a large success in empirical works over a wide variety of disciplines. They have been applied for instance in macroeconomics to the analysis of the business cycle (Hamilton, 1989;McConnell and Perez-Quiros, 2000) and monetary policy (Sims and Zha, 2006), in finance to asset pricing (Cecchetti et al, 1990(Cecchetti et al, , 1993, in environmental science to characterize wind time series (Ailliot and Monbet, 2012), in medicine for clinical monitoring (Gordon and Smith, 1990), in speech recognition (Juang and Rabiner, 1985), as well as in many other fields. General discussions and additional references can be found in West and Harrison (1997), Kim and Nelson (1999), Scott (2002), Fruhwirth-Schnatter (2006), and Ang and Timmermann (2011).…”

Section: Introductionmentioning

confidence: 99%

Marginal distribution of Markov-switchingVARprocesses

Fiorentini

Planas

Rossi

2016

Communications in Statistics - Theory and Methods

View full text Add to dashboard Cite

We make available simple and accurate closed-form approximations to the marginal distribution of Markov-switching vector auto-regressive (MS VAR) processes. The approximation is built upon the property of MS VAR processes of being Gaussian conditionally on any semi-infinite sequence of the latent state. Truncating the semi-infinite sequence and averaging over all possible sequences of that finite length yields a mixture of normals that converges to the unknown marginal distribution as the sequence length increases. Numerical experiments confirm the viability of the approach which extends to the closely related class of MS state space models. Several applications are discussed.

show abstract

Markov-switching autoregressive models for wind time series

Cited by 103 publications

References 27 publications

A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales

A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales

Bayesian Estimation of Multivariate Autoregressive Hidden Markov Model with Application to Breast Cancer Biomarker Modeling

Marginal distribution of Markov-switchingVARprocesses

Contact Info

Product

Resources

About