Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions

Prado, Raquel; West, Mike

doi:10.1007/978-1-4612-0699-6_31

Cited by 19 publications

(22 citation statements)

References 3 publications

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“…Various related autoregressive component models have been discussed by West (1996West ( , 1997 and West and Harrison (1997, Section 9.5). In the current paper, we explore and exploit new classes of smoothness priors, introduced recently by Huerta and West (1999), in developing Bayesian spectral inference.…”

Section: Introductionmentioning

confidence: 98%

Bayesian Inference on Periodicities and Component Spectral Structure in Time Series

Huerta

West

1999

Journal Time Series Analysis

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We detail and illustrate time series analysis and spectral inference in autoregressive models with a focus on the underlying latent structure and time series decompositions. A novel class of priors on parameters of latent components leads to a new class of smoothness priors on autoregressive coef®cients, provides for formal inference on model order, including very high order models, and leads to the incorporation of uncertainty about model order into summary inferences. The class of prior models also allows for subsets of unit roots, and hence leads to inference on sustained though stochastically time-varying periodicities in time series. Applications to analysis of the frequency composition of time series, in both time and spectral domains, is illustrated in a study of a time series from astronomy. This analysis demonstrates the impact and utility of the new class of priors in addressing model order uncertainty and in allowing for unit root structure. Time-domain decomposition of a time series into estimated latent components provides an important alternative view of the component spectral characteristics of a series. In addition, our data analysis illustrates the utility of the smoothness prior and allowance for unit root structure in inference about spectral densities. In particular, the framework overcomes supposed problems in spectral estimation with autoregressive models using more traditional model-®tting methods.

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Section: Introductionmentioning

confidence: 98%

Bayesian Inference on Periodicities and Component Spectral Structure in Time Series

Huerta

West

1999

Journal Time Series Analysis

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“…Other studies were concerned with automated model detection in online monitoring data [4,9,17,[24][25][26][27][28][29] in order to forecast the patient state and avoid disasters.…”

Section: An Overview On Time Series Technology Used In Medical Datamentioning

confidence: 99%

Intelligent Monitoring System for Intensive Care Units

Nouira

Trabelsi

2011

J Med Syst

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We address in the present paper a medical monitoring system designed as a multi-agent based approach. Our system includes mainly numerous agents that act as correlated multi-agent sub-systems at the three layers of the whole monitoring infrastructure, to avoid non informative alarms and send effective alarms at time. The intelligence in the proposed monitoring system is provided by the use of time series technology. In fact, the capability of continuous learning of time series from the physiological variables allows the design of a system that monitors patients in real-time. Such system is a contrast to the classical threshold-based monitoring system actually present in the Intensive Care Units (ICUs) which causes a huge number of irrelevant alarms.

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“…In addition, dynamic components of nonstationary processes can be handled through dynamic linear models (DLM) which is developed by West and Harrison [23]. A significant model of DLM is that of TVAR models, which is called latent process model (LPM) [24][25][26][27][28][29][30] In this study, sEMG signals recorded under two different movement conditions are analyzed using the latent process model (LPM) which combines TVAR models and DLM. Decompositions of sEMG signals and the TVAR parameters over time, such as wavelength and modulus trajectories of the latent, are extracted to classify the movement conditions.…”

Section: Introductionmentioning

confidence: 99%

A novel method of non-stationary sEMG signal analysis and decomposition using a latent process model

Song

Xie

Wu³

et al. 2013

2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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To solve the problems of conventional signal analysis methods about non-stationary and frequency characteristics of surface electromyogrphy (sEMG) is of great significance to rehabilitation robot control with EMG-based human-computer interfaces (HCI). In this paper, the latent process models of sEMG signals were developed based on the combination of time-varying auto-regression (TVAR) model and dynamic linear model (DLM), which decomposed the signals into several components, and each component represents different timefrequency behavior of sEMG signals. On the basis of the latent process model, time-varying parameters, modulus and wavelength features were extracted. The fusing features of sEMG signals in two elbow movement conditions (elbow flexion and elbow extension) were adopted for clustering analysis and classification of data was visualized by using self-organizing map (SOM). An experiment with 9 healthy participants was carried out to verify the validity of this algorithm. The result implied that latent process model is a meaningful and valuable non-stationary sEMG signal analysis method which may be promising in rehabilitation robot control.

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Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions

Cited by 19 publications

References 3 publications

Bayesian Inference on Periodicities and Component Spectral Structure in Time Series

Bayesian Inference on Periodicities and Component Spectral Structure in Time Series

Intelligent Monitoring System for Intensive Care Units

A novel method of non-stationary sEMG signal analysis and decomposition using a latent process model

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