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This paper attempts to apply the ARMA (Auto Regressive Moving Average) model to predict patients' glucose concentration because of the successful application of time series ARMA model in forecasting the fault rate. This paper gives the glucose concentration prediction method based on ARMA model and the actualization on MATLAB platform. After analyzing the time series of several patients' glucose concentration,...
The objective of this paper was to forecast the number of monthly new outbreaks of Swine Pasteurellosis with ARMA model. The forecasting model was constructed using the data from Jan. 2005 to Dec. 2008 and was validated by the data in 2009. The method employed inspection of the run plots and ACF plots in the determination of the order d of differencing and the parameters of ARMA model. Normalized...
In the competitive petroleum markets, oil price forecasting is becoming increasingly relevant to producers and consumers. This paper develops a structural econometric model of the Brent crude spot price using the explanatory variable of defined relative inventory and OPEC production to analyze and forecast short-run oil price. A Hodrick-Prescott filter method presented obtains the relative inventory...
Monthly Malaysia crude oil production data for the period of January 2005 to May 2010 were analyzed using time-series method called Autoregressive Integrated Moving Average (ARIMA) model. Autocorrelation and partial autocorrelation functions were calculated to examine the stationarity of the data. Then, an appropriate Box-Jenkins ARIMA model was fitted. Validity of the model was tested using Box-Pierce...
Magnetic storm is a significant magnetic disturbance, which has some influence in communication system and power system. Its intensity is always measured by DST and it is often predicted by the application of neural network nonlinear simulation and differential equation so on. Most of these methods need the data collected several hours before magnetic storm besides DST data. Here we proposed a new...
The aim of this work is to analyze factors influencing electricity consumption in Japan using regression analysis. Every season regression models are developed for forecasting and determining elasticity coefficient associated with climatic conditions. As explanation variables, we use temperature, relative humidity and other factors such us holidays. Then, several statistical tests, for instance, t-test,...
The presence of multiple services sharing a common functional interface necessitates differentiating between them on the basis of their performance. However advertised quality of service (QoS) alone cannot paint the true picture of how the service has performed so far and how it will continue to function in the near future. This information is crucial for service selection. In this paper, we outline...
In order to improve the accuracy of spatial forecasting based on panel data, the significance of spatial autocorrelation on the panel data is tested by Moran I, the first-order spatial autoregressive model and the Kriging algorithm model are established from the perspective of the cross-sectional data, respectively, and back-propagation neural network model trained by the genetic algorithm is established...
In this paper, the stochastic characteristics of the electric consumption in France are analyzed. It is shown that the load time series exhibit lasting abrupt changes in the stochastic pattern, termed breaks, which need to be accounted for during the modeling process. Thus, a new robust diagnostic approach for which the identification of the breaks is carried out via a robust autocorrelation function...
Using spatial autoregressive model, the spatial characteristic between power demand and GDP is analyzed in this paper. And the combination model of forecasting in which the spatial characteristic between power requirement was considered is established. Simulations results show the distinct spatial dependence between power requirement and GDP, and the dependence between power requirements and GDP is...
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