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Forecasting of Henry Hub Natural Gas Spot Price by using ARIMA
Model With and Without Discrete Wavelet Transform
Tan Pui Man
Supervisor: Dr. Hassilah Binti Salleh
Bachelor of Science (Financial Mathematics)
School of Informatics and Applied Mathematics
Natural gas, as the cleanest burning fossil fuel leads to increasingly importance of its
price forecasting due to growing concern about pollution. Hence, combination of wavelet
approach and time series forecasting are tested in this research to predict natural gas
price. ARIMA is implemented as the forecasting model but it can be easily affected by the
noise in time series without pre-processing the original data. Therefore, discrete wavelet
transform (DWT) is used as a pre-processing technique for denoising. DWT decomposes
the original signal into series of approximation and detail components then reconstructed
them by filtering of threshold value. The Henry Hub natural gas (HHNG) weekly spot price
as a benchmark for US market, are used for this study. The forecasted results of ARIMA
with and without DWT are compared. Finally, the result indicates that the employment of
DWT is capable in improving the performance of HHNG weekly spot price forecasting.
896 | UMT UNDERGRADUATE RESEARCH DAY 2018