Forecasting of water intake and supply in water plant in Johor

The safety and availability of water are important for public health, domestic use, food, and drink production process. Since water is essential in daily life, the demand for water intake and water supply are increasing. Moreover, it must be ensured that the water intake is sufficient to supply water towards the consumers. This is vital to avoid water scarcity in society. Thus, the main objective of this research is to forecast the water intake and water supply by adopting exponential smoothing and Box-Jenkins methods. Then, the forecast performance is evaluated by using mean error (ME), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE). Further, the best model is used to forecast water intake and water supply for the next seven days. The daily data for three years of water intake and water supply in one of the water plants in Johor was collected. The forecast capabilities of the two different methods were compared. Both methods are fitted well but, in overall, the triple exponential smoothing method is outperformed compared to the Box-Jenkins method. This is due to the exponential smoothing method to produce less MAPE and ME values. Both datasets shared the same model which imply the water treatment system used is stable and in good condition. Besides, the water intake and water supply by using triple exponential smoothing method is predicted to be decreased in the following seven days

Error magnitude and directional accuracy for time series forecasting evaluation

Evaluation of forecast accuracy is very much influenced by the choice of accurate measurement since it can produce different conclusion from the empirical results. Thus, it is important to use appropriate measurement in accordance to the purpose of forecasting. Commonly, accuracy is measured in terms of error magnitude. However, directional accuracy is as important as error magnitude especially in economics since it considers directional movement of the data. This research attempted to combine the two types of measurements by introducing a new element, the slope value. This proposed measure is known as square error modified of directional accuracy (SE-mDA). Before that, the existing directional change error measurement was modified by comparing the direction of two subsequent forecasts data with two subsequent observed data. Empirical application utilizing the monthly data of Malaysia and Bali tourism demand was used to compare the forecast performance between SARIMA, time series regression, Holt-Winter, intervention neural network and fuzzy time series. The root mean square error, mean absolute percentage error, mean absolute deviation, Fisher’s exact test, Chi-square test, directional accuracy, directional value and the modified of directional change error were used in forecast accuracy evaluation. The best forecast model in terms of SE-mDA for the data of Malaysia and Bali are Holt-Winters and neural network, respectively. The main conclusion from this study is that SE-mDA is able to improve the forecasting performance assessment of error magnitude measurement by considering the directional movements. At the same time it also enhances the available directional accuracy measurement by taking into account the difference between slopes of forecast data and observed data. These improvements will help forecaster to choose the best forecasting method or model so as to produce the most accurate forecast

Malaysia tourism demand forecasting using box-jenkins approach

Tourism industry in Malaysia is crucial and has contributes a huge part in Malaysia’s economic growth. The capability of forecasting field in tourism industry can assist people who work in tourism-related-business to make a correct judgment and plan future strategy by providing the accurate forecast values of the future tourism demand. Therefore, this research paper was focusing on tourism demand forecasting by applying Box-Jenkins approach on tourists arrival data in Malaysia from 1998 until 2017. This research paper also was aiming to produce the accurate forecast values. In order to achieve that, the error of forecast for each model from Box-Jenkins approach was measured and compared by using Akaike Information Criterion (AIC), Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Model that produced the lowest error was chosen to forecast Malaysia tourism demand data. Several candidate models have been proposed during analysis but the final model selected was SARIMA (1,1,1)(1,1,4)12. It is hoped that this research will be useful in forecasting field and tourism industry

Improving skills in rounding off the whole number

This study was conducted to address teaching and learning skills in rounding off a whole number. This study consisted of 15 years 4 students from the Kong Nan Chinese Primary School, Parit Raja, Johor, Malaysia. Initial survey to identify this problem was carried out by analyzing the exercise books and exercises in pre-test. Based on these analyses, a large number of students were not proficient in relevant skills. A ‘q’ technique was introduced as an approach in teaching and learning to help students master the skills of rounding whole numbers. In summary, this technique helps students to remember the sequence of processes and process in rounding numbers. A total of four sessions of teaching and learning activities that take less than an hour have been implemented specifically to help students to master this technique. Results of the implementation of these activities have shown very positive results among the students. Two post tests were carried out to see the effectiveness of techniques and the results shows that 100% of students were able to answer correctly at least three questions correctly. The t-test analysis was clearly showed the effectiveness of ‘q’ technique. This technique also indirectly helps to maintain and increase student interest in learning Mathematics. This is shown with the active involvement of students in answering questions given by the teacher

A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income

Analysis by human perception could not be solved using traditional method since uncertainty within the data have to be dealt with first. Thus, fuzzy structure system is considered. The objectives of this study are to determine suitable cluster by using fuzzy c-means (FCM) method, to apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression (FLR) as proposed by Tanaka and Ni and to improve the FCM method and FLR model proposed by Zolfaghari to predict manufacturing income. This study focused on FLR which is suitable for ambiguous data in modelling. Clustering is used to cluster or group the data according to its similarity where FCM is the best method. The performance of models will measure by using the mean square error (MSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE). Results shows that the improvisation of FCM method and FLR model obtained the lowest value of error measurement with MSE=1.825 11 10 , MAE=115932.702 and MAPE=95.0366. Therefore, as the conclusion, a new hybrid of FCM method and FLR model are the best model for predicting manufacturing income compared to the other model

Alternative method outlier treatments with Box-Jenkins and neural network via interpolation method

Outliers represent the points that greatly diverge and act differently from the rest of the points. These kinds of phenomenon usually happen in the data especially in time series data. The presence of this outlier gave bad effect in all statistical method including forecasting if there are no actions on it. Thus, this paper discusses alternative methods which are linear interpolation and cubic spline interpolation to the time series data as outlier treatment. Assuming outlier as missing value in the data, the outlier were detected and the results were compared using forecast accuracies by two popular forecasting model, Box-Jenkins and neural network. The monthly time series data of Malaysia tourist arrival were used in this paper from 1998 until 2015. The result indicates that the improved time series data using the linear interpolation and cubic spline interpolation showed great performance in forecasting than the original data series

Treatment of outlier using interpolation method in Malaysia tourist arrivals

The presence of outliers is an example of aberrant data that can have huge negative influence on statistical method under the assumption of normality and it affects the estimation. This paper introduces an alternative method as outlier treatment in time series which is interpolation. It compares two interpolation methods using performance indicator. Assuming outlier as a missing value in the data allows the application of the interpolation method to interpolate the missing value, thus comparing the result using the forecast accuracy. The monthly time series data from January 1998 until December 2015 of Malaysia Tourist Arrivals were used to deal with outliers. The results found that the cubic spline interpolation method gave the best result than the linear interpolation and the improved time series data indicated better performance in forecasting rather than the original time series data of Box-Jenkins model

Comparative analysis for performance measurements of software testing between mobile applications and web applications

Software testing has an important role in software engineering, and is fundamental to Software Quality Assurance (SQA). Besides the popularity of web applications, mobile applications have gained paralleled advancement despite increasing complexity. On one hand, this issue reflects the rising concerns for ensuring performance both of web and mobile applications. On the other hand, a comparative analysis of software testing issues between web and mobile applications has not been completed. Thus, this study aims to employ an effective testing approach that is able to adapt both of web and mobile application testing to detect possible failures. To achieve this, UML activity diagrams were developed from four case studies for web and mobile applications to describe the behaviour of those applications. Test cases were then generated by using the MBT technique from the developed UML activity diagrams. Performance measurements Hits per Second, Throughput and Memory Utilization for each case study were evaluated by execution of test cases that were generated by using HP LoadRunner 12.02 tool. Finally, the MSE of performance measurements was compared and analysed among the four case studies. The experimental results showed that the disparity between the mobile applications and web applications was obvious. Based on the comparison analysis for software testing of mobile applications versus web applications that was the web applications were lesser than mobile applications for software testing of four case studies in terms each of the Hits per Second, Throughput and Memory Utilization. Consequently, mobile applications need more attention in the testing process