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Congress on climate change: Global risks, challenges and decisions; Copenhagen (Denmark); 10-12 Mar 2009; Available from http://dx.doi.org/10.1088/1755-1307/6/6/062021; Abstract only; Country of input: International Atomic Energy Agency (IAEA)
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IOP Conference Series: Earth and Environmental Science (EES); ISSN 1755-1315;
; v. 6(6); [1 p.]

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AbstractAbstract
No abstract available
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Congress on climate change: Global risks, challenges and decisions; Copenhagen (Denmark); 10-12 Mar 2009; Available from http://dx.doi.org/10.1088/1755-1307/6/29/292026; Abstract only; Country of input: International Atomic Energy Agency (IAEA)
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Journal Article
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IOP Conference Series: Earth and Environmental Science (EES); ISSN 1755-1315;
; v. 6(29); [1 p.]

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AbstractAbstract
[en] In this paper the testing of normality for un unconditionally heterosedastic macroeconomic time series is studied. It is underlined that the classic al Jarque-Bera test (JB hereafter) for normality is inadequate in our framework. On the other hand it is found that the approach which consists in correcting the heteroscedasticity by kernel smoothing for testing normality is justified asymptotically. Nevertheless it appears from Monte Carlo experiments that such a methodology can noticeably suffer from size distortion for samples that are typical for macroeconomic variables. As a consequence a parametric bootstrap methodology for correcting the problem is proposed. The innovations distribution of a set of inflation measures for the U.S., Korea and Australia are analyzed.
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789 p; 2019; 21 p; ITISE 2019: International Conference on Time Series and Forecasting; Granada (Spain); 25-27 Sep 2019; Available https://itise.ugr.es/ITISE2019_Vol1.pdf
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[en] The orbital motion of an artificial satellite or space debris object is perturbed by a variety, and sometimes not well-modeled, external forces [1, 2]. The hybrid methodology can be used to predict these unmodeled effects or the uncertainty associated with this process. In this work, a Hybrid Orbit Propagator based on SGP4 [3–7] and a state space formulation of the exponential smoothing method as the forecasting technique is developed. The error terms of the forecasting technique are considered Gaussian noise what allows us to use the maximum likelihood method to estimate the parameters of the exponential smoothing model, as well as computing the point forecast and the reliable predictive intervals. Finally, this Hybrid Orbit Propagator is applied to data from a satellite of the Galileo constellation. This Propagator improves the accuracy of the classical SGP4 and it is particularly good for short forecast horizons.
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789 p; 2019; 2 p; ITISE 2019: International Conference on Time Series and Forecasting; Granada (Spain); 25-27 Sep 2019; Available https://itise.ugr.es/ITISE2019_Vol1.pdf
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Book
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AbstractAbstract
[en] A ''dynamo theory'' technique appears to successfully predict decadal time scale solar activity variations. The technique was developed a decade ago, following some puzzling correlations involved with geomagnetic ''precursors'' of solar activity. Based upon this, a dynamo theory method was developed to predict solar activity. The method was used successfully in solar cycle. We now see sunspot numbers increasing at an alarming rate and realize 1) that a large cycle is developing and 2) that the cycle may even surpass the largest cycle-19. We use a ''Sporer Butterfly'' method to show that the cycle can now be expected to peak in the latter half of 1989, consistent with an amplitude comparable to our value predicted near the last solar minimum
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European Space Agency, 75 - Paris (France); 691 p; Dec 1988; p. 333-338; Symposium on seismology of the sun and sun-like stars; Tenerife (Spain); 26-30 Sep 1988
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Report
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Palma, J.; Jimenez, F.; Sanchez, G.; Marin Garcia, D.; Palacios, F.; Lopez-Rodriguez, L.
ITISE 2019. Proceedings of papers. Vol 12019
ITISE 2019. Proceedings of papers. Vol 12019
AbstractAbstract
[en] In this paper we propose a methodology to build a model for predicting future outbreaks of Methicilin-resistant Staphylococcus aereus (MRSA). Infection incidence forecasting is approached as a feature selection based time series forecasting problem using multivariate time series composed of incidence of Staphylococcus Aereus and MRSA infections, influenza incidence and total days of therapy of both of Levoflaxin and Oseltamivir antimicrobials.
Primary Subject
Source
789 p; 2019; 12 p; ITISE 2019: International Conference on Time Series and Forecasting; Granada (Spain); 25-27 Sep 2019; Available https://itise.ugr.es/ITISE2019_Vol1.pdf
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Book
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Saint Fleur, B.E.; Artigue, G.; Johannet, A.; Pistre, S.
ITISE 2019. Proceedings of papers. Vol 12019
ITISE 2019. Proceedings of papers. Vol 12019
AbstractAbstract
[en] Flash floods frequently hit Southern France and cause heavy damages and fatalities. To better protect persons and goods, official flood forecasting services in France need accurate information and efficient models to optimize their decision and policy. Since heavy rainfalls that cause such floods are very heterogeneous, it becomes a serious challenge for forecasters. Such phenomena are typically nonlinear and more complex than classical floods events. That problem leads to consider complementary alternatives to enhance the management of such situations. For decades, artificial neural networks have been very efficient to model nonlinear phenomena, particularly rainfall-discharge relations in various types of basins. They are applied in this study with two main goals: first modelling flash floods on the Gardon de Mialet basin; second, extract internal information from the model by using the Knowledge eXtraction method to provide new ways to improve models. The first analysis shows that the kind of nonlinear predictor influences strongly the representation of information: e.g. the main influent variable (rainfall) is more important in the recurrent and static models than in the feed-forward one. For understanding flash floods genesis, recurrent and static models appear thus as better candidates, even if their results are better.
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Source
789 p; 2019; 12 p; ITISE 2019: International Conference on Time Series and Forecasting; Granada (Spain); 25-27 Sep 2019; Available https://itise.ugr.es/ITISE2019_Vol1.pdf
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Book
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Conference
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[en] This paper considers modelling of a non-stationary integervalued autoregressive moving average of order 1 (INARMA(1,1)) model by assuming that the innovation follows a Poisson and negative binomial distribution. Two simulation experiments are also conducted to assess the performance of conditional maximum likelihood (CML) and generalized quasi-likelihood (GQL) estimation methods.
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Source
789 p; 2019; 10 p; ITISE 2019: International Conference on Time Series and Forecasting; Granada (Spain); 25-27 Sep 2019; Available https://itise.ugr.es/ITISE2019_Vol1.pdf
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Book
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Conference
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AbstractAbstract
No abstract available
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Congress on climate change: Global risks, challenges and decisions; Copenhagen (Denmark); 10-12 Mar 2009; Available from http://dx.doi.org/10.1088/1755-1307/6/46/462005; Abstract only; Country of input: International Atomic Energy Agency (IAEA)
Record Type
Journal Article
Literature Type
Conference
Journal
IOP Conference Series: Earth and Environmental Science (EES); ISSN 1755-1315;
; v. 6(46); [1 p.]

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AbstractAbstract
[en] This paper considers the approximate factor model for high-dimensional time series with additive outliers. We propose a robustification procedure of the information criteria proposed by [1]. The robust estimator of the number of factors is obtained by replacing the standard covariance matrix with M-covariance matrix. Simulations are carried out under the scenarios of multivariate time series with and without additive outliers to assess the impact of additive outliers on the standard information criteria and to analyze the finite sample size performance of the proposed robust estimator of the number of factors.
Primary Subject
Source
789 p; 2019; 12 p; ITISE 2019: International Conference on Time Series and Forecasting; Granada (Spain); 25-27 Sep 2019; Available https://itise.ugr.es/ITISE2019_Vol1.pdf
Record Type
Book
Literature Type
Conference
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