Universiti Teknologi Malaysia Institutional Repository: No conditions. Results ordered -Date Deposited. 2020-02-23T16:52:28ZEPrintshttp://eprints.utm.my/images/sitelogo.gifhttp://eprints.utm.my/id/eprint/2015-10-15T01:09:34Z2017-08-21T07:04:48Zhttp://eprints.utm.my/id/eprint/id/eprint/48177This item is in the repository with the URL: http://eprints.utm.my/id/eprint/id/eprint/481772015-10-15T01:09:34ZCombine holts winter and support vector machines in forecasting time serieThis study proposes on a combine methodology that exploits the Holts- Winter (HW) model and the Support Vector Machines (SVM) model in forecasting time series. Problems of forecasting using time series data have been and still being addressed at every sphere of research using different approaches. The performance of the forecast was compared among the three models, the HW model, the SVM model and the combine model (HW and SVM). Four different data sets namely, airline passengers’ data, machinery industry production data, clothing industry data and sugar production data were considered in the study. The statistical measures such as mean squared error (MSE), mean average error (MAE) and correlation coefficient, R, were used to evaluate the performance of the propose model. The result of this study indicated that the combine model shows an improvement of 149.3% over HW model and 35.9% improvement over the SVM model for the airline passengers’ data. The result of the machinery industry presented that the combine model shows an improvement of 93.3% over HW model and 42.8% improvement over the SVM model. In the case of the clothing industry the result shows the combine model gives an improvement of 61.6% over HW model and 12.0% improvement over SVM model. Lastly, with respect to the sugar production, the result shows that the combine model indicated an improvement of 34.4% over HW model and 25.1% improvement over SVM model. Therefore the results of the experiments suggest that the proposed combine model is more reliable in time series when compared with the individual modelsMohammed Salisu Alfa2015-06-10T01:57:20Z2017-09-19T06:25:02Zhttp://eprints.utm.my/id/eprint/id/eprint/45453This item is in the repository with the URL: http://eprints.utm.my/id/eprint/id/eprint/454532015-06-10T01:57:20ZPersepsi masyarakat parlimen Pekan terhadap gagasan 1MalaysiaMalaysia merupakan negara yang terdiri daripada masyarakat yang berbilang etnik, kaum dan agama. Perbezaan ini memerlukan kepada satu gagasan yang mampu mengeratkan hubungan sedia ada. Justeru, pihak kerajaan melancarkan gagasan 1Malaysia dengan matlamat meningkatkan tahap hubungan sedia ada. Oleh itu, kajian ini dijalankan untuk mengetahui sejauh manakah penerimaan rakyat terhadap gagasan 1Malaysia yang dilancarkan oleh kerajaan, mengetahui halangan dan cabaran dalam merealisasikan gagasan 1Malaysia, mengenal pasti medium yang efektif dalam proses penyampaian gagasan 1Malaysia ke akar umbi dan menghasilkan model yang komprehensif untuk memajukan gagasan 1Malaysia. Bagi mencapai objektif kajian tersebut, kumpulan penyelidik telah menjalankan kajian kuantitatif dan kualitatif iaitu menggunakan instrumen soal selidik dan temu bual. Data yang diperoleh dianalisis menggunakan perisian SPSS dan N’Vivo 8.0. Dapatan kajian ini menunjukkan penerimaan rakyat terhadap gagasan 1Malaysia berada pada tahap yang tinggi. Kajian ini memberi manfaat kepada banyak pihak terutama pihak kerajaan dalam usaha memajukan gagasan 1Malaysia dan meningkatkan keharmonian hubungan antara kaum di negara ini.Kamarul Azmi JasmiAzmi Shah SuratmanRamli AwangSulaiman KadikonHussin SalamonAminuddin RuskamSalleh RosmanNasrul Hisyam Nor MuhammadNorlina MuhammadBushrah BasironSayed Mahussain Sayed AhmadAhmad Muhyiddin HassanMohd. Nasir Ripin2015-04-27T04:50:03Z2017-01-31T06:08:47Zhttp://eprints.utm.my/id/eprint/id/eprint/44977This item is in the repository with the URL: http://eprints.utm.my/id/eprint/id/eprint/449772015-04-27T04:50:03ZImproving short term load forecasting using double seasol arima modelForecasting load demand with high accuracy is required to avoid energy wasting and prevent system failure. The aim of this paper is to develop a forecasting model based on double SARIMA for improving the accuracy of short term load prediction in Malaysia and compare the results with single SARIMA model. A half hourly load demand of Malaysia for 4 months, from September 01, 2005 to December 31, 2005 is used in this study with the mean absolute percentage error (MAPE) as one of the accuracy measures. The results of the identification step show that the load data have two seasonal periods, i.e. daily and weekly seasonality with length 48 and 336 respectively. The estimation and diagnostic check steps show that the best order of double SARIMA for half hourly load demand of Malaysia is ARIMA([2,3,4,8,11,16,18,19,20,21,28,29,30,32,40,41,45,46,47],1,1)(0,1,1)48(0,1,1)336 with in-sample and out-sample MAPE values of 0.96840 and 4.49251 respectively. The in-sample and out-sample MAPE of a single SARIMA model are 1.07872 and 10.45530 respectively. Thus, the current study shows that the double SARIMA model performs better than single SARIMA model since the MAPE of in-sample and out-sample are reduced by 10.22676% and 57.03126% respectively.Norizan MohamedMaizah Hura AhmadSuhartono SuhartonoZuhaimy Ismail2015-04-21T03:31:18Z2017-01-31T06:53:29Zhttp://eprints.utm.my/id/eprint/id/eprint/44931This item is in the repository with the URL: http://eprints.utm.my/id/eprint/id/eprint/449312015-04-21T03:31:18ZForecasting short term load demand using double seasol arima modelLoad demand is a time series data and it is one of the major input factors in economic development especially in a developing COlUltry such as Malaysia. Forecasting load demand with high accuracy is hoped to help the cOlmtry, especially the Malaysian electricity utility company to generate an appropriate load of required power supply which can avoid energy wasting and prevent system failure. A half hourly load demand of Malaysia for one year, from September 01, 2005 to August 31, 2006 measured in Megawatt (MW) is used for this study with the mean absolute percentage error (1.1APE) as a forecasting accuracy. Statistical Analysis System, SAS package was used to analyze the data. The best model was selected based on the mean absolute percentage error (1.1APE) and the theoretical autocorrelation fWlction (ACF) was presented to prove that the best model satisfies the load data. The ARIMA(O,I,1 XO, 1,1 )48(0,1,1 )336 with in-sample MAPE of 0.9906% was selected as the best model for this study. Comparing the forecasting performances by using k-step ahead outsample forecasts and one-step ahead forecasts, we fOWld that the 1.1APE for the one-step ahead out-sample forecasts from any horizon were all less than 1%. In other words it can be concluded that the one-step ahead out-sample forecasts was more accurate. There was a reduction in 1.1APE percentages for all lead time horizons considered, ranging between 89% to 96%. Furthermore a time series plot of out-samples of actual load data, kstep ahead and one-step ahead out-sample forecasts showed that one-step ahead out-sample forecasts followed the actual load data more closely than k-step ahead out-sample forecasts. Therefore we propose that the theoretical ACF must be considered in proving the best model satisfies load demand and that the one-step ahead out-sample forecasts must also be considered in forecasting load, especially in Malaysia load data.Norizan MohamedMaizah Hura AhmadSuhartono Suhartono