Riswan Efendi

A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand

The fuzzy logical relationships and the midpoints of interval have been used to determine the numerical in-out-samples forecast in the fuzzy time series modeling. However, the absolute percentage error is still yet significantly improved. This can be done where the linguistics time series values should be forecasted in the beginning before the numerical forecasted values obtained. This paper introduces the new approach in determining the linguistic out-sample forecast by using the index numbers of linguistics approach. Moreover, the weights of fuzzy logical relationships are also suggested to compensate the presence of bias in the forecasting. The daily load data from National Electricity Board (TNB) of Malaysia is used as an empirical study and the reliability of the proposed approach is compared with the approach proposed by Yu. The result indicates that the mean absolute percentage error (MAPE) of the proposed approach is smaller than that as proposed by Yu. By using this approach the linguistics time series forecasting and the numerical time series forecasting can be resolved.

IMPROVED WEIGHT FUZZY TIME SERIES AS USED IN THE EXCHANGE RATES FORECASTING OF US DOLLAR TO RINGGIT MALAYSIA

Foreign exchange rate (forex) forecasting has been the subject of several rigorous investigations due to its importance in evaluating the benefits and risks of the international business environments. Many methods have been researched with the ultimate goal being to increase the reliability and efficiency of the forecasting method. However as the data are inherently dynamic and complex, the development of accurate forecasting method remains a challenging task if not a formidable one. This paper proposes a new weight of the fuzzy time series model for a daily forecast of the exchange rate market. Through this method, the weights are assigned to the fuzzy relationships based on a probability approach. This can be implemented to carry out the frequently recurring fuzzy logical relationship (FLR) in the fuzzy logical group (FLG). The US dollar to the Malaysian Ringgit (MYR) exchange rates are used as an example and the efficiency of the proposed method is compared with the methods proposed by Yu and Cheng et ...

Application of Fuzzy Time Series Approach in Electric Load Forecasting

In electrical power management, load forecasting accuracy is an indispensable factor which influences the decision making and planning of power companies in the future. Previous research has explored various forecasting models to resolve this issue, ranging from linear and non-linear regression to artificial intelligence algorithm. However, the absolute percentage error has yet to significantly improve using these models. Through this paper, the fuzzy time series (FTS) model was suggested to obtain better forecasted values and increases the forecasting accuracy. This accuracy could be obtained by using effective length of intervals of the discourse universe. The yearly dataset of Taiwan regional electric load was used for this empirical study and the reliability of the proposed model was compared with other previous models. The results indicated that the mean absolute percentage error (MAPE) of the proposed model (FTS) is smaller than MAPE obtained from those previous models.

Estimation of Confidence-Interval for Yearly Electricity Load Consumption Based on Fuzzy Random Auto-Regression Model

Many models have been implemented in the energy sectors, especially in the electricity load consumption ranging from the statistical to the artificial intelligence models. However, most of these models do not consider the factors of uncertainty, the randomness and the probability of the time series data into the forecasting model. These factors give impact to the estimated model’s coefficients and also the forecasting accuracy. In this paper, the fuzzy random auto-regression model is suggested to solve three conditions above. The best confidence interval estimation and the forecasting accuracy are improved through adjusting of the left-right spreads of triangular fuzzy numbers. The yearly electricity load consumption of North-Taiwan from 1981 to 2000 are examined in evaluating the performance of three different left-right spreads of fuzzy random auto-regression models and some existing models, respectively. The result indicates that the smaller left-right spread of triangular fuzzy number provides the better forecast values if compared with based line models.

INTER-QUARTILE RANGE APPROACH TO LENGTH–INTERVAL ADJUSTMENT OF ENROLLMENT DATA IN FUZZY TIME SERIES FORECASTING

Various methods have been presented to investigate the length of data interval and partition number of universe of discourse in fuzzy time series to achieve high level forecasting accuracy. However, the interval length is still an issue and thus, in°uencing the forecasting accuracy. This paper proposes a new approach using the average inter-quartile range to improve the interval length and subsequently the partition number of universe of discourse. Moreover, in forecasting

Implementation of Fuzzy Time Series in Forecasting of the Non-Stationary Data

To forecast the non-stationary data is quite difficult when compared with the stationary data time series. Because their variances are not constant and not stable like the second data type. This paper presents the implementation of fuzzy time series (FTS) into the non-stationary time series data forecasting, such as, the electricity load demand, the exchange rates, the enrollment university and others. These data forecasts are derived by implementing of the weightage and linguistic out-sample methods. The result shows that the FTS can be applied in improving the accuracy and efficiency of these non-stationary data forecasting opportunities.

Decision Support Model in Determining Factors and Its Dominant Criteria Affecting Cholesterol Level Based on Rough-Regression

The statistical regression models have been frequently used to explain the causal relationship between exogenous factors and the cholesterol level of patients. While, the dominant criteria for each exogenous factor which give impact to the cholesterol level are not yet investigated by previous studies. In this paper, we are interested to introduce a decision making model based on rough-regression in handling the significant contribution between the dominant criteria, exogenous and endogenous factors, respectively. The result showed the proposed model is able to investigate the dominant criteria and factors affecting cholesterol level patients. This model may help the counterparts in the decision making.

A new procedure in stock market forecasting based on fuzzy random auto-regression time series model

Various models used in stock market forecasting presented have been classified accord- ing to the data preparation, forecasting methodology, performance evaluation, and perfor- mance measure. However, these models have not sufficiently discussed in data prepara- tion to overcome randomness, as well as uncertainty and volatility of stock prices issues in achieving high forecasting accuracy. Therefore, the focus of this paper is the data prepa- ration procedure of triangular fuzzy number to build an improved fuzzy random auto- regression model using non-stationary stock market data for forecasting purposes. The im- proved forecasting model considers two types of input, which are data with low-high and single point values of stock market prices. Even though, low-high data present variabil- ity and volatility in nature, the single data has to be form in symmetry left-right spread to present variability and standard error. Then, expectations and variances, confidence- intervals of fuzzy random data are constructed for fuzzy input-output data. By using the input-output data and simplex approach, parameters of the model can be estimated. In this study, some real data sets were used to represent both types of inputs, which are the Kuala Lumpur stock exchange and Alabama University enrollment. The study found that variability and spread adjustment are important factors in data preparation to improve ac- curacy of the fuzzy random auto-regression model.

Prediction of Malaysian–Indonesian Oil Production and Consumption Using Fuzzy Time Series Model

Fuzzy time series has been implemented for data prediction in the various sectors, such as education, finance-economic, energy, traffic accident, others. Moreover, many proposed models have been presented to improve the forecasting accuracy. However, the interval-length adjustment and the out-sample forecast procedure are still issues in fuzzy time series forecasting, where both issues are yet clearly investigated in the previous studies. In this paper, a new adjustment of the interval-length and the partition number of the data set is proposed. Additionally, the determining of the out-sample forecast is also discussed. The yearly oil production (OP) and oil consumption (OC) of Malaysia and Indonesia from 1965 to 2012 are examined to evaluate the performance of fuzzy time series and the probabilistic time series models. The result indicates that the fuzzy time series model is better than the probabilistic models, such as regression time series, exponential smoothing in terms of the forecasting accuracy. This paper thus highlights the effect of the proposed interval length in reducing the forecasting error significantly, as well as the main differences between the fuzzy and probabilistic time series models.

Forecasting of Malaysian Oil Production and Oil Consumption Using Fuzzy Time Series

Many statistical models have been implemented in the energy sectors, especially in the oil production and oil consumption. However, these models required some assumptions regarding the data size and the normality of data set. These assumptions give impact to the forecasting accuracy. In this paper, the fuzzy time series (FTS) model is suggested to solve both problems, with no assumption be considered. The forecasting accuracy is improved through modification of the interval numbers of data set. The yearly oil production and oil consumption of Malaysia from 1965 to 2012 are examined in evaluating the performance of FTS and regression time series (RTS) models, respectively. The result indicates that FTS model is better than RTS model in terms of the forecasting accuracy.