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Forecasting Malaysia Load Using a Hybrid Model Norizan Mohamed; Maizah Hura Ahmad
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 10, No 1 (2010)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v10i1.1003

Abstract

A hybrid model, which combines the seasonal time series ARIMA (SARIMA) and the multilayer feedforwardneural network to forecast time series with seasonality, is shown to outperform both twosingle models. Besides the selection of transfer functions, the determination of hidden nodes to usefor the non linear model is believed to improve the accuracy of the hybrid model. In this paper, wefocus on the selection of the appropriate number of hidden nodes on the non linear model to forecastMalaysia load. Results show that by using only one hidden node, the hybrid model of Malaysia loadperforms better than both single models with mean absolute percentage error (MAPE) of less than 1%.
Identification of Time Series Model: An Application Part Wan Muhamad Amir Bin W Ahmad; Norhayati Rosli; Norizan Mohamed; Zalila Binti Ali
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 1 (2006)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v6i1.931

Abstract

Time series analysis generally referred to any analysis which involved to a time series data. In thisanalysis, any of the continuous observation is commonly dependent. If the continuous observation isdependable, then the values that will come are able to be forecasted from the previous observation(Weir 2006). If the behaviour of coming time series are able to be exactly forecasted based on previoustimes series, so it’s called deterministic time series. The objective of times series can be summarizedas to find the statistical model to describe the behaviour of the time series data and afterwards madeuse of skilled statistical techniques for estimation, forecasting but also the controlling. The use oftime series analysis very much spread in various fields like biology, medical and many more that hada purpose for forecasting. In this paper the recognition of concerning the Autoregressive Processmodel AR (p), Moving Average Process MA (q), Autoregressive Moving Average ARMA (p,q),Autoregressive Integrated Moving Average ARIMA (p,d,q) was given attention through the approach tothe Autocorrelation Function ACF and Partial Autocorrelation Function (PACF) theory plot.
Proportional Hazard Regression Analysis By Using Survival Data Wan Muhamad Amir Bin W Ahmad; Norizan Mohamed; Zurairah Ahmad; Mustafa bin Mamat
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 5, No 1 (2005)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v5i1.913

Abstract

Recently a number of papers have considered both longitudinal changes in a variable and theassociated effect on the length of time to the occurrence of an event (Schork and Remington, 2003).Longitudinal research is performed to study a phenomenon as it is evolving over time. Thephenomenon will generally show changes over time, but it may also show stability. Researchers inpsychology often use longitudinal designs to assess change. Various statistical techniques have beenused to analyze these data, including proportional hazard regression. This paper illustrates the use ofthe SPSS to examine blood data with this technique, as well. The advantages of using a Cox modelapproach to blood pressure analysis are discussed.
An Artificial Neural Networks Forecasting for Malaysia’s Load Norizan Mohamed; Maizah Hura Ahmad; Zuhaimy Ismail; Khairil Anuar Arshad
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 8, No 2 (2008)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v8i2.985

Abstract

In this paper, two artificial neural networks models, namely the multilayer feedforward neuralnetwork and the recurrent neural network are applied for Malaysia's load forecasting. A half hourlyload data is divided equally into three distinct sets for training, validation and testing.Backpropagation is selected as the learning algorithm whereas the transfer function for both hiddenlayer and output layer is sigmoid the function. The forecasting performances were compared betweenthese two models. The results show that, the sum squared error (SSE) of multilayer feedforwardneural network were the lowest hence the multilayer feedforward neural network is a better model fora half hourly Malaysia's load.
Measurement System Analysis Using Repeatability and Reproducibility Techniques Norizan Mohamed; Yamene Davahran
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 1 (2006)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v6i1.932

Abstract

To monitor and improve any manufacturing process in quality control, it is important to measure thecause of the attribute for that process. The quality of measurement data depends on the repeatedmeasurements obtained through measurement systems which will operate at a given condition. Thisstudy uses a type of statistical quality control technique called Repeatability and Reproducibility(R&R) to determine how much of the process variation is caused by the variation of the measurementsystem that is being used. The two types of charts that were used for this study were the ANOVAchart and the Mean & Average chart.
Carta Kawalan XmR Dan Median : Satu Penyelesaian Untuk Data Pencong Norizan Mohamed; Wan Muhamad Amir Bin W Ahmad; Rinner Masli
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 7, No 1 (2007)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v7i1.948

Abstract

Carta kawalan banyak digunakan dalam kawalan mutu berstatistik. Tujuan menggunakan cartakawalan ini adalah untuk memastikan sesuatu proses berada dalam keadaan terkawal dan stabilsepanjang masa secara grafik. Carta kawalan ini terdiri daripada had-had kawalan iaitu, hadkawalan atas, had kawalan tengah dan had kawalan bawah. Untuk mengetahui sesuatu prosestersebut adalah terkawal, kesemua titik yang mewakili variasi dalam sesuatu proses berada di dalamlingkungan had atas dan had bawah. Jika terdapat titik yang terkeluar daripada had kawalan atasmahupun had kawalan bawah, maka proses tersebut dikatakan tidak terkawal dan tidak stabil.Salah satu penggunaan carta kawalan yang meluas ialah carta kawalan XmR iaitu X mewakili nilaiindividu manakala mR mewakili peralihan julat. Untuk memastikan sesuatu proses tersebut beradadalam keadaan terkawal dan stabil, anggapan kenormalan mestilah dipenuhi. Namun demikian,kebanyakan proses pengeluaran menghasilkan data yang pencong dan ini menyebabkan prosestersebut tidak terkawal dan tidak stabil. Oleh itu, satu pendekatan berdasarkan kuasa penjelmaanakan turut dibincangkan dalam kajian ini.