Hybrid SSA-TSR-ARIMA for water demand forecasting
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Hybrid SSA-TSR-ARIMA for water demand forecasting |
| 2. | Creator | Author's name, affiliation, country | Suhartono Suhartono; Institut Teknologi Sepuluh November; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Salafiyah Isnawati; Institut Teknologi Sepuluh November; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Novi Ajeng Salehah; Institut Teknologi Sepuluh November; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Dedy Dwi Prastyo; Institut Teknologi Sepuluh November; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Heri Kuswanto; Institut Teknologi Sepuluh November; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Muhammad Hisyam Lee; Universiti Teknologi Malaysia; Malaysia |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Singular spectrum analysis; Time series regression; Automatic ARIMA; Hybrid method; Water demand forecasting |
| 4. | Description | Abstract | 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. |
| 5. | Publisher | Organizing agency, location | Universitas Ahmad Dahlan |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2018-11-11 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijain.org/index.php/IJAIN/article/view/275 |
| 10. | Identifier | Digital Object Identifier (DOI) | https://doi.org/10.26555/ijain.v4i3.275 |
| 11. | Source | Title; vol., no. (year) | International Journal of Advances in Intelligent Informatics; Vol 4, No 3 (2018): November 2018 |
| 12. | Language | English=en | en |
| 13. | Relation | Supp. Files | |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2018 Suhartono Suhartono, Salafiyah Isnawati, Novi Ajeng Salehah, Dedy Dwi Prastyo, Heri Kuswanto, Muhammad Hisyam Lee This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |