Modified weighted for enrollment forecasting based on fuzzy time series

The enrollment study is main point for the university planning. Many previous studies have been presented for enrollment forecasting. This paper proposed the adoption the weighted and the difference between actual data toward midpoint interval based on fuzzy time series. By using the enrollment of the University of Alabama and Universiti Teknologi Malaysia (UTM), as data sets for training and testing, the performance of the adoption approach has been shown much improvement in terms of MSE (Mean Square Error) and average error of forecasting measurements

Penggunaan Pendekatan Sistem-S dan ESSYNS dalam Analisis Taburan Normal

The purpose of this paper is to illustrate the use of S-systems methodology and ESSYNS software in the area of computational statistics. First, we demonstrate how the densities are formulated as S-systems, then we derive representations for cumulatives and quantiles. Cumulatives are obtained with little extra effort and quantiles are readily calculated from the inverted S-system. These representations are then solved numerically using ESSYNS. The density and the cumulative curves for the normal distribution are generated for the values in the range (-3,3). The S-system method produces many quantiles in a single computer run. The use of S-system generalizations and approximation has shown to be a useful alternative method for analysing distributions

Application of taguchi method in the optimization of reacttive mobile ad hoc networks routing procotols parameters

A Mobile Ad-Hoc Network (MANET) is a collection of wireless nodes forming a temporary network without the aid of any established infrastructure or centralized administration [2]. Establishing a route from a source node to a destination node is a fundamental problem in MANETs. In general, routes between nodes in MANET may include multiple hops. Each node will able to communicate directly with any other node that resides within its transmission range. For communicating with nodes that resides beyond this range, the node needs to use intermediate nodes to relay the messages hop by hop. Routing protocols play a significant role in this kind of network by selecting the route to forward the packets and in establishing a connectio

Applying Penalized Binary Logistic Regression with Correlation Based Elastic Net for Variables Selection

Reduction of the high dimensional classification using penalized logistic regression is one of the challenges in applying binary logistic regression. The applied penalized method, correlation based elastic penalty (CBEP), was used to overcome the limitation of LASSO and elastic net in variable selection when there are perfect correlation among explanatory variables. The performance of the CBEP was demonstrated through its application in analyzing two well-known high dimensional binary classification data sets. The CBEP provided superior classification performance and variable selection compared with other existing penalized methods. It is a reliable penalized method in binary logistic regression

Non-transformed principal component technique on weekly construction stock market price

The fast-growing urbanization has contributed to the construction sector be- coming one of the major sectors traded in the world stock market. In general, non- stationarity is highly related to most of the stock market price pattern. Even though stationarity transformation is a common approach, yet this may prompt to originality loss of the data. Hence, the non-transformation technique using a generalized dynamic principal component (GDPC) were considered for this study. Comparison of GDPC was performed with two transformed principal component techniques. This is pertinent as to observe a larger perspective of both techniques. Thus, the latest weekly two-years observations of nine constructions stock market price from seven different countries were applied. The data was tested for stationarity before performing the analysis. As a re- sult, the mean squared error in the non-transformed technique shows eight lowest values. Similarly, eight construction stock market prices had the highest percentage of explained variance. In conclusion, a non-transformed technique can also present a better result outcome without the stationarity transformation

Air pollutant index calendar-based graphics for visualizing trends profiling and analysis

Detection of air quality abnormality is important as an early warning system for air quality control and management. The information can raise citizens’ awareness towards current air quality status. By using time series plot, the data pattern can be identified but not able to exactly determine the abnormality due to overcrowded plot. Therefore, visualization data profiling was presented in this study by using seven years Malaysia daily air pollutant index to improve the detection. Result shown, the developed approach can simply identify the poor air quality across the month and year. Malaysia air quality was good and consistent between November and May. However, upward trend existed between June and October due to the forest fire happened in Sumatra. This visualization approach improved air pollution detection profiling and it is useful for related agencies to guide the control actions to be taken. This approach can be applied to any countries and data set to give more competent information

Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production

Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(1 1 ) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(1 1 ) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy

Artificial neural network forecasting performance with missing value imputations

This paper presents time series forecasting method in order to achieve high accuracy performance. In this study, the modern time series approach with the presence of missing values problem is developed. The artificial neural networks (ANNs) is used to forecast the future values with the missing value imputations methods used known as average, normal ratio and also the modified method. The results are validated by using mean absolute error (MAE) and root mean square error (RMSE). The result shown that by considering the right method in missing values problems can improved artificial neural network forecast accuracy. It is proven in both MAE and RMSE measurements as forecast improved from 8.75 to 4.56 and from 10.57 to 5.85 respectively. Thus, this study suggests by understanding the problem in time series data can produce accurate forecast and the correct decision making can be produced

The combination of forecasts with different time aggregation

In forecasting, it is important to improve forecast accuracy. Thus, the forecast combination have been proposed in the literature. Usually, the classical approach in forecast combination obtain from the composite of two (or more) available forecasts with identical timings. However, forecast horizon, short and long term do affect the forecast performance. Therefore, unlike previous combinations, this paper combined the forecasts with different time aggregations in order to capture the unique information of the data set. We had considered the problems in forecasting daily air pollutant index (API) as well as the monthly aggregate, by using the Box-Jenkins method and fuzzy time series methodas the time series approach. Then, the monthly aggregate forecasts were interpolated to obtain the forecasts on a daily basis. Each of the original forecasts was used to determine the weights in forming the combined forecast. The error magnitude measurements were used to measure the accuracy. The result showed that the forecast combinations with different timings outperformed the individual forecasts and traditional forecast combinations with identical timing. Hence, the combination of different timing data sets produced better forecasting accuracy, which can be a good practice in many types of data with different time horizon