Shuhaida Ismail

A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting

Support vector machine is a new tool from Artificial Intelligence (AI) field has been successfully applied for a wide variety of problem especially in time-series forecasting. In this paper, least square support vector machine (LSSVM) is an improved algorithm based on SVM, with the combination of self-organizing maps(SOM) also known as SOM-LSSVM is proposed for time-series forecasting. The objective of this paper is to examine the flexibility of SOM-LSSVM by comparing it with a single LSSVM model. To assess the effectiveness of SOM-LSSVM model, two well-known datasets known as the Wolf yearly sunspot data and the Monthly unemployed young women data are used in this study. The experiment shows SOM-LSSVM outperforms the single LSSVM model based on the criteria of mean absolute error (MAE) and root mean square error (RMSE). It also indicates that SOM-LSSVM provides a promising alternative technique in time-series forecasting.

Empirical Analysis on Sales of Video Games: A Data Mining Approach

This paper studies factors that make the sales of video games becomes a blockbuster. The dataset used is collected from an online database maintained by VGChartz.com. Using the dataset, the Rapid Miner tool is used to select the features or factors and produce efficient estimation of the data. The techniques used in this project included the k- Nearest Neighbour (k-NN), Random Forest and Decision Tree. The factors and differences in the results are deliberated and discussed.

Comparative Analysis of River Flow Modelling by Using Supervised Learning Technique

The goal of this research is to investigate the efficiency of three supervised learning algorithms for forecasting monthly river flow of the Indus River in Pakistan, spread over 550 square miles or 1800 square kilometres. The algorithms include the Least Square Support Vector Machine (LSSVM), Artificial Neural Network (ANN) and Wavelet Regression (WR). The forecasting models predict the monthly river flow obtained from the three models individually for river flow data and the accuracy of the all models were then compared against each other. The monthly river flow of the said river has been forecasted using these three models. The obtained results were compared and statistically analysed. Then, the results of this analytical comparison showed that LSSVM model is more precise in the monthly river flow forecasting. It was found that LSSVM has he higher r with the value of 0.934 compared to other models. This indicate that LSSVM is more accurate and efficient as compared to the ANN and WR model.

Combination model of empirical mode decomposition and SVM for river flow forecasting

A reliable prediction of river flow is always important for sound planning and smooth operation of the water resource system. In this study, a combination models based on Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM) model referred as EMD-SVM is proposed for estimating future value of monthly river flow data. The proposed EMD-SVM has three important stages. The first stage, the data were decomposed into several numbers of Intrinsic Mode Functions (IMF) and a residual using EMD technique. In the second stage, the meaningful signals are identified using a statistical measure and the new dataset are obtained in this stage. The final stage applied SVM as forecasting tool to perform the river flow forecasting. To assess the effectiveness of EMD-SVM model, Selangor and Bernam Rivers were used as case studies. The experiment results stated that the proposed EMD-SVM have outperformed other model based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Correlation Coefficient (r...

Hybrid Empirical Mode Decomposition- ARIMA for Forecasting Exchange Rates

This paper studied the forecasting of monthly Malaysian Ringgit (MYR)/ United State Dollar (USD) exchange rates using the hybrid of two methods which are the empirical model decomposition (EMD) and the autoregressive integrated moving average (ARIMA). MYR is pegged to USD during the Asian financial crisis causing the exchange rates are fixed to 3.800 from 2nd of September 1998 until 21st of July 2005. Thus, the chosen data in this paper is the post-July 2005 data, starting from August 2005 to July 2010. The comparative study using root mean square error (RMSE) and mean absolute error (MAE) showed that the EMD-ARIMA outperformed the single-ARIMA and the random walk benchmark model.

Empirical mode decomposition coupled with least square support vector machine for river flow forecasting

This paper aims to investigate the ability of Empirical Mode Decompositio n (EMD) coupled with Least Square Support Vector Machine (LSSVM) model in order to improve the accuracy of river flow forecasting. To assess the effectiveness of this model, Bernam monthly river flow data, has served as the case study. The proposed model was set at three important stages which are decomposition, component identification and forecasting stages respectively. The first stage is known as decomposition stage where EMD were employed for decomposing the dataset into several numbers of Intrinsic Mode Functions (IMF) and a residue. During on second stage, the meaningful signals are identified using a statistical measure and the new dataset are obtained in this stage. The final stage applied LSSVM as a forecasting tool to perform the river flow forecasting. The performance of the EMD coupled with LSSVM model is compared with the single LSSVM models using various statistics measures of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation-coefficient (R) and Correlation of Efficiency (CE). The comparison results reveal the proposed model of EMD coupled with LSSVM model serves as a useful tool and a promising new method for river flow forecasting.