Results 1 - 10 of 91
Results 1 - 10 of 91. Search took: 0.01 seconds
|Sort by: date | relevance|
[en] We present a bottom-up derivation of methods for fluctuation analysis with detrending and claim their basic principles. Such methods detect long-range correlations in time series even in the presence of additive trends or intrinsic nonstationarities.
[en] The transport sector requires a reduction in CO2 emissions, so that industry, policy makers and researchers are forced to think about the diffusion of plug-in electric vehicles. Market forecasting is a well-developed field of study, but in the electric vehicle domain this is a complex task due to the relative newness of the market.
[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.
[en] In this article events of productivity of the insurance data in Jordan will be explored and forecasted using some of traditional model which is Exponential model (EM) compound with Wavelet transform (WT) in order to improve the forecasting accuracy. The decomposed dataset will be collected form Amman Stock Exchange (ASE) from Jordan. As a result the forecasting accuracy will be improved by using EM
[en] In linear regression models where there are no relationships between the dependent variable and each of the potential explanatory variables – a usual scenario in real-world problems – some of them can be identified as relevant by standard statistical procedures. This incorrect identification is usually known as Freedman’s paradox. To avoid this disturbing effect in regression analysis, an info-metrics approach based on normalized entropy is discussed and illustrated in this work. The results suggest that normalized entropy is a powerful alternative to traditional statistical methodologies currently used by practitioners.
[en] Data preprocessing methods: Data decomposition, seasonal adjustment, singular spectrum analysis, detrending methods, etc., Econometric models, Preferable Oral, Real macroeconomic monitoring and forecasting, Real time macroeconomic monitoring and forecasting.
[en] The relationship between autoregressive moving-average (ARMA) models in discrete time and the corresponding models in continuous time is examined in this paper. The linear stochastic models that are commonly regarded as the counterparts of the ARMA models are driven by a forcing function that consists of the increments of a Wiener Process. This function is unbounded in frequency.
[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.