Enhanced single exponential smoothing technique for extreme data
Flood is an extreme event that causes damage to properties and loss of human life. Extreme event time series is usually nonlinear pattern. This produces high fluctuations in signal and large uncertainty in forecast quality. Single Exponential Smoothing Technique (SEST) is used for time series foreca...
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QA299.6-433 Analysis Noor Shahifah, Muhamad Enhanced single exponential smoothing technique for extreme data |
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Flood is an extreme event that causes damage to properties and loss of human life. Extreme event time series is usually nonlinear pattern. This produces high fluctuations in signal and large uncertainty in forecast quality. Single Exponential Smoothing Technique (SEST) is used for time series forecasting and to smooth out the random variation in signal. However, this technique comes with its own weaknesses to handle extreme data, especially with the presence of outliers. Based on the limitation, the enhancement of the SEST using Membership Function (MF) graph in Fuzzy Logic
(FL) is proposed. The MF graph is used to transform the actual data into new representation which SEST can accept. This technique allows the SEST to use the extreme dataset after the transformation process. The MF is used to transform the actual data into the value in the range of zero to one, while SEST is used to smooth out the irregular component in time series. Six datasets of river water level and three datasets of temperature level were used for this experiment. These techniques have been integrated for handling extreme data. The combination between MF and SEST (MF+SEST) in data smoothing process has shown good performance in handling five river water level data and three temperature data compared to standard SEST. This
combination technique has also improved the performances of Neural Network (NN) model and is capable of producing accurate prediction. MF+SEST has improved the NN performances by an average of 95% compared to SEST and produce more consistent results. This technique can be used in extreme data preprocessing to improve the NN performances to produce an accurate prediction. The prediction
generated will be able to facilitate the authority and public in preparing for upcoming flood event. |
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Enhanced single exponential smoothing technique for extreme data |
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Enhanced single exponential smoothing technique for extreme data |
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Enhanced single exponential smoothing technique for extreme data |
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Enhanced single exponential smoothing technique for extreme data |
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enhanced single exponential smoothing technique for extreme data |
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Awang Had Salleh Graduate School of Arts & Sciences |
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my-uum-etd.57662021-04-04T08:09:18Z Enhanced single exponential smoothing technique for extreme data 2015 Noor Shahifah, Muhamad Mohamed Din, Aniza Abu Hassan, Zorkeflee Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA299.6-433 Analysis Flood is an extreme event that causes damage to properties and loss of human life. Extreme event time series is usually nonlinear pattern. This produces high fluctuations in signal and large uncertainty in forecast quality. Single Exponential Smoothing Technique (SEST) is used for time series forecasting and to smooth out the random variation in signal. However, this technique comes with its own weaknesses to handle extreme data, especially with the presence of outliers. Based on the limitation, the enhancement of the SEST using Membership Function (MF) graph in Fuzzy Logic (FL) is proposed. The MF graph is used to transform the actual data into new representation which SEST can accept. This technique allows the SEST to use the extreme dataset after the transformation process. The MF is used to transform the actual data into the value in the range of zero to one, while SEST is used to smooth out the irregular component in time series. Six datasets of river water level and three datasets of temperature level were used for this experiment. These techniques have been integrated for handling extreme data. The combination between MF and SEST (MF+SEST) in data smoothing process has shown good performance in handling five river water level data and three temperature data compared to standard SEST. This combination technique has also improved the performances of Neural Network (NN) model and is capable of producing accurate prediction. MF+SEST has improved the NN performances by an average of 95% compared to SEST and produce more consistent results. This technique can be used in extreme data preprocessing to improve the NN performances to produce an accurate prediction. The prediction generated will be able to facilitate the authority and public in preparing for upcoming flood event. 2015 Thesis https://etd.uum.edu.my/5766/ https://etd.uum.edu.my/5766/1/depositpermission_s813599.pdf text eng public https://etd.uum.edu.my/5766/2/s813599_01.pdf text eng validuser masters masters Universiti Utara Malaysia Abdullah, S., Sapii, N., Dir, S., & Jalal, T. M. T. (2012). Application of univariate forecasting models of tuberculosis cases in Kelantan. In 2012 International Conference on Statistics in Science, Business, and Engineering (ICSSBE) (pp. 1–7). doi:10.1109/ICSSBE.2012.6396582 Adnan, R., Ruslan, F.A., Samad, A.M. & Md-Zain, Z. (2012). Flood Water Level Modeling and Prediction Using Artificial Neural Network: Case Study of Sungai Batu Pahat in Johor. 2012 IEEE Control and System Graduate Research Colloquium (ICSGRC 2012). Agboola, A. H., Gabriel, A. J., Aliyu, E. O., & Alese, B. K. (2013). Development of a Fuzzy Logic based Rainfall Prediction Model. 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