Abstract
Time series analysis has attracted a lot of attention of researchers in recent times due to availability of sophisticated computing facilities. In this paper wavelet transformation and seasonal ARIMA methodology have been used to analyze and forecast time series. First we analyze time series data for gas demand of Sui southern Gas Company (SSGC) of Pakistan and forecast with Box-Jenkins SARIMA models then we look at waveletbased multiresolution analysis (MRA) and SARIMA models predictions using the compressed and de-noised signals. With the right choice of mother wavelets, this method is very successful in analyzing and forecasting time series. In the later part of this paper we compare forecast performance of the three models in consideration.
Keywords
Time Series Analysis, SARIMA, Wavelets, Multiresolution Analysis
DOI
https://doi.org/10.54784/1990-6587.1100
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Burney, S. A., Saleemi, A., & Raza, S. A. (2006). Wavelet based SARIMA models for forecasting natural gas demand. Business Review, 1(1), 134-139. Retrieved from https://doi.org/10.54784/1990-6587.1100
