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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Nureize Arbaiy
Universiti Tun Hussein Onn

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Forecasting ASEAN countries exchange rates using auto regression model based on triangular fuzzy number Hamijah Mohd Rahman; Nureize Arbaiy; Riswan Efendi; Chuah Chai Wen
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i3.pp1525-1532

Abstract

Exchange rate forecasting is important to represent the expectation of exchange rates future values. The forecasting task is due to the economic factor and the historical data used to forecast are exposed to uncertainty and observational error during data collection. The existing auto regression model only deals with uncertainty exist in the model, not in the data preparation. Uncertainties may contained in the data input and should be treated during data preparation which is an early stage of forecasting process. To date, only few researches discuss intensely on a fuzzy data preparation. However, data treatment during data preparation is important to reduce model’s error due to uncertainty problem. Hence, this paper presents an approach to construct Triangular Fuzzy Number to handle uncertainty in data during data preparation. As the Triangular Fuzzy Number is often used to represent uncertain information in a form of interval, this study proposed a procedure to construct Triangular Fuzzy Number from single point data. In this study, the Triangular Fuzzy Number is built in a form of symmetric triangular with 1%, 3% and 5% spread value. Autoregressive model is then used to forecast the exchange rate of Association of South East Asian Nation (ASEAN) countries. The result in this study shows that the forecasting exchange rate is significantly important to trace the movement of ASEAN countries exchange rates and beneficial in forecasting planning.
A simulation study of first-order autoregressive to evaluate the performance of measurement error based symmetry triangular fuzzy number Muhammad Shukri Che Lah; Nureize Arbaiy
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1559-1567

Abstract

Data collected by various data collection methods are often exposed to uncertainties that may affect the information presented by quantitative results. This also causes the forecasted model developed to be less precise because of the uncertainty contained in the input data used. Hence, preparing the data by means of handling inherent uncertainties is necessary to avoid the developed forecasting model to be less accurate. Traditional autoregressive (AR) model uses precise values and deals with the uncertainty normally in forecasting model. Fewer researches are focused on data preparation in time-series autoregressive for handling the uncertainties in data. Hence, this paper proposes a procedure to perform data preparation to handle uncertainty. The fuzzy data preparation involves the construction of fuzzy symmetric triangle numbers using percentage error and standard deviation method. The proposed approach is evaluated by using the simulation method for first-order autoregressive, AR (1) model in terms of forecasting accuracy performance. Simulation result demonstrates that the proposed approach obtains smaller error in forecasting and hence achieving better forecasting accuracy and dealing with uncertainty in the analysis.
Co-Authors Chuah Chai Wen Hamijah Mohd Rahman Muhammad Shukri Che Lah Riswan Efendi