Egypt has faced a major problem in balancing electricity produced and electricity consumed at any... more Egypt has faced a major problem in balancing electricity produced and electricity consumed at any time in the day. Therefore, short-term forecasts are required for controlling and scheduling of electric power system. Electricity demand series has more than one seasonal pattern. Double seasonality of the electricity demand series in many countries have considered. Double seasonality pattern of Egyptian electricity demand has not been investigated before. For the first time, different double seasonal autoregressive integrated moving average (DSARIMA) models are estimated for forecasting Egyptian electricity demand using maximum likelihood method. í µí±«í µí±ºí µí±¨í µí±¹í µí±°í µí±´í µí±¨(µí±¨(í µí¿, í µí¿, í µí¿) (í µí¿ , í µí¿, í µí¿) í µí¿í µí¿ (í µí¿ , í µí¿ , í µí¿) í µí¿í µí¿í µí¿ model is selected based on Schwartz Bayesian Criterion (SBC). In addition, empirical results indicated the accuracy of the forecasts produced by this model for different time horizon.
Forecasting electricity demand is critical concerning future technical improvements. A notable fe... more Forecasting electricity demand is critical concerning future technical improvements. A notable feature of the electricity demand time series is the presence of both intraday and intraweek seasonal cycles. This study investigates using double seasonal Holt-Winters exponential smoothing method for forecasting hourly electricity demand in Egypt. A one year of hourly electricity demand measured in Megawatt from 7 January 2010 to 31 December 2010 is used. The mean absolute percentage error is used to compare forecasting accuracy between the double seasonal Holt-Winters method and the traditional Holt-Winters that considers only single seasonality pattern. The forecasts produced by the double seasonal Holt-Winters method outperform those obtained from single seasonal Holt-Winters methods.
A major drawback of First Order Stochastic Dominance approach is dominance indetermination. Levy ... more A major drawback of First Order Stochastic Dominance approach is dominance indetermination. Levy and Leshno in 2002 suggested Almost Stochastic Dominance as a remedy in the uni-dimensional case. We introduce a Generalization of Almost First and second Order Dominance (MAFOD and MASOD) to the multidimensional case with application on child wellbeing in Egypt. We perform a multidimensional (FOD) analysis on seven deprivation indicators for three age-groups of children from Egypt 2014 Demographic and Health Survey (EDHS14). This methodology allows the ordinal ranking of regions and governorates of Egypt in terms of their children wellbeing based on their probability of domination. To solve the dominance indetermination we apply MAFOD and MASOD.
In this paper a Bayesian analysis of the Double Seasonal Autoregressive Model is proposed. Using ... more In this paper a Bayesian analysis of the Double Seasonal Autoregressive Model is proposed. Using conjugate and Jeffreys’ priors, the marginal posterior distribution of the model parameters is shown to be t-distribution, while the marginal posterior distribution of the variance is an inverse gamma distribution. Using an extensive simulation study, the Bayesian estimators prove to be accurate. In addition, the proposed Bayesian methodology is applied to the internet traffic data set.
Economic, practical, or physical constraints sometimes prevent the factor space of a designed exp... more Economic, practical, or physical constraints sometimes prevent the factor space of a designed experiment from being a regular p-dimensional hypercube or hypersphere. Since standard designs may not be the best choice, it is desirable to be able to find best designs under these restrictions. This paper extends the work of Zahran et al. (J. Quality Tech. 35 (4)) using a
Under section 303(d) of the Clean Water Act, states must identify water segments where loads of p... more Under section 303(d) of the Clean Water Act, states must identify water segments where loads of pollutants are violating numeric water quality standards. Consequences of misidentification are quite important. A decision that water quality is impaired initiates the total maximum daily load or TMDL planning requirement. Falsely concluding that a water segment is impaired results in unnecessary TMDL planning and pollution control implementation costs. On the other hand, falsely concluding that a segment is not impaired may pose a risk to human health or to the services of the aquatic environment. Because of the consequences, a method is desired that minimizes or controls the error rates. The most commonly applied approach is to use the Environmental Protection Agency (EPA)'s raw score approach in which a stream segment is listed as impaired when greater than 10 per cent of the measurements of water quality conditions exceed a numeric criteria. An alternative to the EPA approach is ...
Communications in Statistics - Theory and Methods, 2001
Time series classification is a very rich subject and may be found in many areas such as economic... more Time series classification is a very rich subject and may be found in many areas such as economics, business, chemistry, engineering, and environmental data. This paper proposes an approximate Bayesian scheme to assign a univariate time series realization into one of several autoregressive moving average sources, with unknown coefficients and precision, that share a common unknown order. Using either a normal gamma density or a Jeffreys' prior and an approximate conditional likelihood function, the proposed assignment technique is to develop the marginal posterior mass function of a classification vector assuming the maximum of the unknown order is known. A time series realization is assigned to the r-th autoregressive moving average source whenever the classification vector has its largest value at the r-th mass point. A simulation study is carried out in order to examine the behavior and adequacy of the proposed technique with moderate realization sizes. The selected sources are chosen in such a way to include different parameters. The numerical results show that the proposed technique is practical, easy to program, and efficient in handling the classification problem of autoregressive moving average sources with unknown order.
Communications in Statistics - Theory and Methods, 2010
A multivariate extension of the adaptive exponentially weighted moving average (AEWMA) control ch... more A multivariate extension of the adaptive exponentially weighted moving average (AEWMA) control chart is proposed. The new multivariate scheme can detect small and large shifts in the process mean vector effectively. The proposed scheme can be viewed as a smooth combination ...
Egypt has faced a major problem in balancing electricity produced and electricity consumed at any... more Egypt has faced a major problem in balancing electricity produced and electricity consumed at any time in the day. Therefore, short-term forecasts are required for controlling and scheduling of electric power system. Electricity demand series has more than one seasonal pattern. Double seasonality of the electricity demand series in many countries have considered. Double seasonality pattern of Egyptian electricity demand has not been investigated before. For the first time, different double seasonal autoregressive integrated moving average (DSARIMA) models are estimated for forecasting Egyptian electricity demand using maximum likelihood method. í µí±«í µí±ºí µí±¨í µí±¹í µí±°í µí±´í µí±¨(µí±¨(í µí¿, í µí¿, í µí¿) (í µí¿ , í µí¿, í µí¿) í µí¿í µí¿ (í µí¿ , í µí¿ , í µí¿) í µí¿í µí¿í µí¿ model is selected based on Schwartz Bayesian Criterion (SBC). In addition, empirical results indicated the accuracy of the forecasts produced by this model for different time horizon.
Forecasting electricity demand is critical concerning future technical improvements. A notable fe... more Forecasting electricity demand is critical concerning future technical improvements. A notable feature of the electricity demand time series is the presence of both intraday and intraweek seasonal cycles. This study investigates using double seasonal Holt-Winters exponential smoothing method for forecasting hourly electricity demand in Egypt. A one year of hourly electricity demand measured in Megawatt from 7 January 2010 to 31 December 2010 is used. The mean absolute percentage error is used to compare forecasting accuracy between the double seasonal Holt-Winters method and the traditional Holt-Winters that considers only single seasonality pattern. The forecasts produced by the double seasonal Holt-Winters method outperform those obtained from single seasonal Holt-Winters methods.
A major drawback of First Order Stochastic Dominance approach is dominance indetermination. Levy ... more A major drawback of First Order Stochastic Dominance approach is dominance indetermination. Levy and Leshno in 2002 suggested Almost Stochastic Dominance as a remedy in the uni-dimensional case. We introduce a Generalization of Almost First and second Order Dominance (MAFOD and MASOD) to the multidimensional case with application on child wellbeing in Egypt. We perform a multidimensional (FOD) analysis on seven deprivation indicators for three age-groups of children from Egypt 2014 Demographic and Health Survey (EDHS14). This methodology allows the ordinal ranking of regions and governorates of Egypt in terms of their children wellbeing based on their probability of domination. To solve the dominance indetermination we apply MAFOD and MASOD.
In this paper a Bayesian analysis of the Double Seasonal Autoregressive Model is proposed. Using ... more In this paper a Bayesian analysis of the Double Seasonal Autoregressive Model is proposed. Using conjugate and Jeffreys’ priors, the marginal posterior distribution of the model parameters is shown to be t-distribution, while the marginal posterior distribution of the variance is an inverse gamma distribution. Using an extensive simulation study, the Bayesian estimators prove to be accurate. In addition, the proposed Bayesian methodology is applied to the internet traffic data set.
Economic, practical, or physical constraints sometimes prevent the factor space of a designed exp... more Economic, practical, or physical constraints sometimes prevent the factor space of a designed experiment from being a regular p-dimensional hypercube or hypersphere. Since standard designs may not be the best choice, it is desirable to be able to find best designs under these restrictions. This paper extends the work of Zahran et al. (J. Quality Tech. 35 (4)) using a
Under section 303(d) of the Clean Water Act, states must identify water segments where loads of p... more Under section 303(d) of the Clean Water Act, states must identify water segments where loads of pollutants are violating numeric water quality standards. Consequences of misidentification are quite important. A decision that water quality is impaired initiates the total maximum daily load or TMDL planning requirement. Falsely concluding that a water segment is impaired results in unnecessary TMDL planning and pollution control implementation costs. On the other hand, falsely concluding that a segment is not impaired may pose a risk to human health or to the services of the aquatic environment. Because of the consequences, a method is desired that minimizes or controls the error rates. The most commonly applied approach is to use the Environmental Protection Agency (EPA)'s raw score approach in which a stream segment is listed as impaired when greater than 10 per cent of the measurements of water quality conditions exceed a numeric criteria. An alternative to the EPA approach is ...
Communications in Statistics - Theory and Methods, 2001
Time series classification is a very rich subject and may be found in many areas such as economic... more Time series classification is a very rich subject and may be found in many areas such as economics, business, chemistry, engineering, and environmental data. This paper proposes an approximate Bayesian scheme to assign a univariate time series realization into one of several autoregressive moving average sources, with unknown coefficients and precision, that share a common unknown order. Using either a normal gamma density or a Jeffreys' prior and an approximate conditional likelihood function, the proposed assignment technique is to develop the marginal posterior mass function of a classification vector assuming the maximum of the unknown order is known. A time series realization is assigned to the r-th autoregressive moving average source whenever the classification vector has its largest value at the r-th mass point. A simulation study is carried out in order to examine the behavior and adequacy of the proposed technique with moderate realization sizes. The selected sources are chosen in such a way to include different parameters. The numerical results show that the proposed technique is practical, easy to program, and efficient in handling the classification problem of autoregressive moving average sources with unknown order.
Communications in Statistics - Theory and Methods, 2010
A multivariate extension of the adaptive exponentially weighted moving average (AEWMA) control ch... more A multivariate extension of the adaptive exponentially weighted moving average (AEWMA) control chart is proposed. The new multivariate scheme can detect small and large shifts in the process mean vector effectively. The proposed scheme can be viewed as a smooth combination ...
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