Hybrid SSA-TSR-ARIMA for water demand forecasting
Water supply management effectively becomes challenging due to the human population and their needs have been growing rapidly. The aim of this research is to propose hybrid methods based on Singular Spectrum Analysis (SSA) decomposition, Time Series Regression (TSR), and Automatic Autoregressive Int...
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Universitas Ahmad Dahlan
2018
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Online Access: | http://eprints.utm.my/id/eprint/79660/1/MuhammadHisyamLee2018_HybridSSA-TSR-ARIMAforwater.pdf http://eprints.utm.my/id/eprint/79660/ http://dx.doi.org/10.26555/ijain.v4i3.275 |
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my.utm.796602019-01-28T04:58:30Z http://eprints.utm.my/id/eprint/79660/ Hybrid SSA-TSR-ARIMA for water demand forecasting Suhartono, Suhartono Isnawati, S. Salehah, N. A. Prastyo, D. D. Kuswanto, H. Lee, M. H. QA Mathematics Water supply management effectively becomes challenging due to the human population and their needs have been growing rapidly. The aim of this research is to propose hybrid methods based on Singular Spectrum Analysis (SSA) decomposition, Time Series Regression (TSR), and Automatic Autoregressive Integrated Moving Average (ARIMA), known as hybrid SSA-TSR-ARIMA, for water demand forecasting. Monthly water demand data frequently contain trend and seasonal patterns. In this research, two groups of different hybrid methods were developed and proposed, i.e. hybrid methods for individual SSA components and for aggregate SSA components. TSR was used for modeling aggregate trend component and Automatic ARIMA for modeling aggregate seasonal and noise components separately. Firstly, simulation study was conducted for evaluating the performance of the proposed methods. Then, the best hybrid method was applied to real data sample. The simulation showed that hybrid SSA-TSR-ARIMA for aggregate components yielded more accurate forecast than other hybrid methods. Moreover, the comparison of forecast accuracy in real data also showed that hybrid SSA-TSR-ARIMA for aggregate components could improve the forecast accuracy of ARIMA model and yielded better forecast than other hybrid methods. In general, it could be concluded that the hybrid model tends to give more accurate forecast than the individual methods. Thus, this research in line with the third result of the M3 competition that stated the accuracy of hybrid method outperformed, on average, the individual methods being combined and did very well in comparison to other methods. Universitas Ahmad Dahlan 2018 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/79660/1/MuhammadHisyamLee2018_HybridSSA-TSR-ARIMAforwater.pdf Suhartono, Suhartono and Isnawati, S. and Salehah, N. A. and Prastyo, D. D. and Kuswanto, H. and Lee, M. H. (2018) Hybrid SSA-TSR-ARIMA for water demand forecasting. International Journal of Advances in Intelligent Informatics, 4 (3). pp. 238-250. ISSN 2442-6571 http://dx.doi.org/10.26555/ijain.v4i3.275 DOI:10.26555/ijain.v4i3.275 |
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Water supply management effectively becomes challenging due to the human population and their needs have been growing rapidly. The aim of this research is to propose hybrid methods based on Singular Spectrum Analysis (SSA) decomposition, Time Series Regression (TSR), and Automatic Autoregressive Integrated Moving Average (ARIMA), known as hybrid SSA-TSR-ARIMA, for water demand forecasting. Monthly water demand data frequently contain trend and seasonal patterns. In this research, two groups of different hybrid methods were developed and proposed, i.e. hybrid methods for individual SSA components and for aggregate SSA components. TSR was used for modeling aggregate trend component and Automatic ARIMA for modeling aggregate seasonal and noise components separately. Firstly, simulation study was conducted for evaluating the performance of the proposed methods. Then, the best hybrid method was applied to real data sample. The simulation showed that hybrid SSA-TSR-ARIMA for aggregate components yielded more accurate forecast than other hybrid methods. Moreover, the comparison of forecast accuracy in real data also showed that hybrid SSA-TSR-ARIMA for aggregate components could improve the forecast accuracy of ARIMA model and yielded better forecast than other hybrid methods. In general, it could be concluded that the hybrid model tends to give more accurate forecast than the individual methods. Thus, this research in line with the third result of the M3 competition that stated the accuracy of hybrid method outperformed, on average, the individual methods being combined and did very well in comparison to other methods. |
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Article |
author |
Suhartono, Suhartono Isnawati, S. Salehah, N. A. Prastyo, D. D. Kuswanto, H. Lee, M. H. |
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Suhartono, Suhartono Isnawati, S. Salehah, N. A. Prastyo, D. D. Kuswanto, H. Lee, M. H. |
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Suhartono, Suhartono |
title |
Hybrid SSA-TSR-ARIMA for water demand forecasting |
title_short |
Hybrid SSA-TSR-ARIMA for water demand forecasting |
title_full |
Hybrid SSA-TSR-ARIMA for water demand forecasting |
title_fullStr |
Hybrid SSA-TSR-ARIMA for water demand forecasting |
title_full_unstemmed |
Hybrid SSA-TSR-ARIMA for water demand forecasting |
title_sort |
hybrid ssa-tsr-arima for water demand forecasting |
publisher |
Universitas Ahmad Dahlan |
publishDate |
2018 |
url |
http://eprints.utm.my/id/eprint/79660/1/MuhammadHisyamLee2018_HybridSSA-TSR-ARIMAforwater.pdf http://eprints.utm.my/id/eprint/79660/ http://dx.doi.org/10.26555/ijain.v4i3.275 |
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1643658256289103872 |
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13.18916 |