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Hybrid Approaches Based on ARIMA, ANN and Bootstrap Method for
Wind Speed Time Series in Terengganu, Malaysia
Nur Fatin Najwa Binti Abdul Nasir
Supervisor: Assoc. Prof. Dr. Muhamad Safiih Bin Lola
Bachelor of Science (Computational Mathematics)
School of Informatics and Applied Mathematics
Hybrid Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network
(ANN) model is widely used, especially to improve the accuracy of the forecasting model.
However, this hybrid model can become more accurate by constructing the standard
error. In this research, new model of ARIMA and ANN with bootstrap method was
developed and test the effectiveness using data wind speed in Kuala Terengganu. In
order to investigate, the single ARIMA, single ANN and hybrid model with bootstrap
method was developed. The result shows that hybrid model with bootstrap method is the
best alternative to reduce the error for forecasting wind speed time series. Therefore,
this hybrid model with bootstrap method becomes alternative in order to make a model
more accurate as well as efficiency.
979 | UMT UNDERGRADUATE RESEARCH DAY 2018