First Order of Autoregressive Air Pollution Forecasting with Symmetry Triangular Fuzzy Number based on Percentage Error
Muhammad Shukri Che Lah1, Mohammad Haris Haikal Othman2, Nureize Arbaiy3
1Muhammad Shukri Che Lah, Department of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Johor, Malaysia.
2Mohammad Haris Haikal Othman, Department of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Johor, Malaysia.
3Nureize Arbaiy, Department of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Johor, Malaysia.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript Published on 19 June 2019 | PP: 265-269 | Volume-8 Issue-8S June 2019 | Retrieval Number: H10440688S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Autoregressive (AR) models is known best to predict multiple sets of stationary data. Previous AR model uses single point data, though uncertainties does exist in data due to various factor. When the data contain uncertainty, traditional procedure which is developed to handle the single point (crisp) data is insufficient to deal with the uncertain data. Moreover, unresolved uncertainty in data may increase error in prediction model. That is, data collected that contains uncertainty should be adequately treated before being used for analysis. Hence, this study proposes an first order of autoregressive (AR(1)) model building based on symmetry triangular fuzzy number. The triangles are established from percentage error method during data preparation of AR(1) modelling to address the uncertainty issue. In this study, AR(1) model with fuzzy data is built to forecast air pollution. The result of this study demonstrates that the proposed method of building fuzzy triangles for AR(1) model obtain smaller error in prediction. The improvement on the existing data preparation process sought from this study is expected to give benefit in achieving better forecasting accuracy and dealing with uncertainty in the analysis.
Keywords: Left-Right Spread, Symmetry Triangular Fuzzy Number, AR(1), Percentage Error, Air Pollutions.
Scope of the Article: Fuzzy Logics