How to exploit external model of data for parameter estimation?

Kárný, Miroslav; Andrýsek, Josef; Bodini, Alessandro; Guy, Tatiana V.; Kracík, Jan; Ruggeri, Fabrizio

doi:10.1002/acs.886

Cited by 13 publications

(13 citation statements)

References 9 publications

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“…Generally, some pre-simulations for the given data have indicated that higher values on the diagonal of V 0 (k) result in lower MSE in the logarithmic pattern. Indeed, data normalization is highly preferable for DMA, because if time-series are rescaled to fit between 0 and 1, then setting V 0 (k) = I corresponds to reasonably high volatility [112,116,117]. Finally, it should be noticed that if E t are residuals from modelling the independent variable Y t , and e t are residuals from modelling y t , where Y t = a·y t + b, with a and b being some scaling parameters (which corresponds to normalization), then E t = a·e t .…”

Section: Variance Matrixmentioning

confidence: 99%

“…Model 1 consists simply of drivers indicated by the Literature review and indicated in Table 1. However, according to, for example, Alquist et al [117], autoregressive models are very common in the oil price modelling. Therefore, Model 2 is constructed by adding the 1 st lag of WTI to the drivers present in the initial Model 1.…”

Section: Time-varying Parameters Preselectionmentioning

confidence: 99%

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Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework

Drachal

2018

Energies

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This article presents results from modelling spot oil prices by Dynamic Model Averaging (DMA). First, based on a literature review and availability of data, the following oil price drivers have been selected: stock prices indices, stock prices volatility index, exchange rates, global economic activity, interest rates, supply and demand indicators and inventories level. Next, they have been included as explanatory variables in various DMA models with different initial parameters. Monthly data between January 1986 and December 2015 has been analyzed. Several variations of DMA models have been constructed, because DMA requires the initial setting of certain parameters. Interestingly, DMA has occurred to be robust to setting different values to these parameters. It has also occurred that the quality of prediction is the highest for the model with the drivers solely connected with the stock markets behavior. Drivers connected with macroeconomic fundamental indicators have not been found so important. This observation can serve as an argument favoring the hypothesis of the increasing financialization of the oil market, at least in the short-term period. The predictions from other, slightly different modelling variations based on DMA methodology, have happened to be consistent with each other in general. Many constructed models have outperformed alternative forecasting methods. It has also been found that normalization of the initial data, although not necessary for DMA from the theoretical point of view, significantly improves the quality of prediction.

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Section: Variance Matrixmentioning

confidence: 99%

Section: Time-varying Parameters Preselectionmentioning

confidence: 99%

Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework

Drachal

2018

Energies

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“…The distribution of the initial state x 0 should be chosen, taking into account the provided prior knowledge f * . The methodology, proposed in [3], solves the analogous problem of incorporating of external knowledge for the case of parameter estimation. Modifying it for Bayesian state estimation, one can transform the prior (flat) pdf f (x 0 ) into the following one:…”

Section: Prior Knowledge Elicitationmentioning

confidence: 99%

“…Originally such a definition of the knowledge incorporation has been proposed in [3], where detailed explanations can be found. The forms of the function * (x 0 ) and the model Z(d |x 0 ) in (11) depend on the cardinality of the set * , denoted by˚ .…”

Section: Prior Knowledge Elicitationmentioning

confidence: 99%

Prior knowledge processing for initial state of Kalman filter

Suzdaleva

2009

Adaptive Control & Signal

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The paper deals with a specification of the prior distribution of the initial state for Kalman filter. The subjective prior knowledge, used in state estimation, can be highly uncertain. In practice, incorporation of prior knowledge contributes to a good start of the filter. The present paper proposes a methodology for selection of the initial state distribution, which enables eliciting of prior knowledge from the available expert information. The proposed methodology is based on the use of the conjugate prior distribution for models belonging to the exponential family. The normal state-space model is used for demonstrating the methodology. The paper covers processing of the prior knowledge for state estimation, available in the form of simulated data. Practical experiments demonstrate the processing of prior knowledge from the urban traffic control area, which is the main application of the research.

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“…Specifically, porous macromolecular materials have attracted durable attention of the scientific community [ 58 , 63 , 64 , 65 , 66 ] due to their functionality and possibility of mechanical control. Nevertheless, this is quite challenging, for a general task to predict and keep under control [ 67 , 68 , 69 ] the desired properties of the self-assembling materials. Several attempts for investigation of stretched porous polyethylene (PE) filled with LC compounds have been already done [ 56 , 70 , 71 , 72 , 73 ].…”

Section: Introductionmentioning

confidence: 99%

Self-Assembling Behavior of Smart Nanocomposite System: Ferroelectric Liquid Crystal Confined by Stretched Porous Polyethylene Film

et al. 2020

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The control and prediction of soft systems exhibiting self-organization behavior can be realized by different means but still remains a highlighted task. Novel advanced nanocomposite system has been designed by filling of a stretched porous polyethylene (PE) film with pore dimensions of hundreds of nanometers by chiral ferroelectric liquid crystalline (LC) compound possessing polar self-assembling behavior. Lactic acid derivative exhibiting the paraelectric orthogonal smectic A* and the ferroelectric tilted smectic C* phases over a broad temperature range is used as a self-assembling compound. The morphology of nanocomposite film has been checked by Atomic Force Microscopy (AFM). The designed nanocomposite has been studied by polarizing optical microscopy (POM), differential scanning calorimetry (DSC), small and wide-angle X-ray scattering and broadband dielectric spectroscopy. The effect of a porous PE confinement on self-assembling, structural, and dielectric behavior of the chiral LC compound has been established and discussed. While the mesomorphic and structural properties of the nanocomposite are found not to be much influenced in comparison to that of a pure LC compound, the polar properties have been toughly suppressed by the specific confinement. Nevertheless, the electro-optic switching was clearly observed under applied electric field of low frequency (210 V, 19 Hz). The dielectric spectroscopy and X-ray results reveal that the helical structure of the ferroelectric liquid crystal inside the PE matrix is completely unwound, and the molecules are aligned along stretching direction. Obtained results demonstrate possibilities of using stretched porous polyolefins as promising matrices for the design of new nanocomposites.

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How to exploit external model of data for parameter estimation?

Cited by 13 publications

References 9 publications

Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework

Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework

Prior knowledge processing for initial state of Kalman filter

Self-Assembling Behavior of Smart Nanocomposite System: Ferroelectric Liquid Crystal Confined by Stretched Porous Polyethylene Film

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