Hybrid SSA-TSR-ARIMA for water demand forecasting

(1) * Suhartono Suhartono Mail (Institut Teknologi Sepuluh November, Indonesia)
(2) Salafiyah Isnawati Mail (Institut Teknologi Sepuluh November, Indonesia)
(3) Novi Ajeng Salehah Mail (Institut Teknologi Sepuluh November, Indonesia)
(4) Dedy Dwi Prastyo Mail (Institut Teknologi Sepuluh November, Indonesia)
(5) Heri Kuswanto Mail (Institut Teknologi Sepuluh November, Indonesia)
(6) Muhammad Hisyam Lee Mail (Universiti Teknologi Malaysia, Malaysia)
*corresponding author


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.


Singular spectrum analysis; Time series regression; Automatic ARIMA; Hybrid method; Water demand forecasting




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[1] W. J. Cosgrove and D. P. Loucks, “Water management: Current and future challenges and research directions,” Water Resour. Res., vol. 51, no. 6, pp. 4823–4839, Jun. 2015, doi: https://doi.org/10.1002/2014WR016869.

[2] J. Adamowski, H. Fung Chan, S. O. Prasher, B. Ozga-Zielinski, and A. Sliusarieva, “Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada,” Water Resour. Res., vol. 48, no. 1, Jan. 2012, doi: https://doi.org/10.1029/2010WR009945.

[3] A. S. Polebitski and R. N. Palmer, “Seasonal residential water demand forecasting for census tracts,” J. Water Resour. Plan. Manag., vol. 136, no. 1, pp. 27–36, 2009, doi: https://doi.org/10.1061/(ASCE)WR.1943-5452.0000003.

[4] S. P. Zhang, H. Watanabe, and R. Yamada, “Prediction of Daily Water Demands by Neural Networks,” 1994, pp. 217–227, doi: https://doi.org/10.1007/978-94-017-3083-9_17.

[5] J. Bougadis, K. Adamowski, and R. Diduch, “Short-term municipal water demand forecasting,” Hydrol. Process. An Int. J., vol. 19, no. 1, pp. 137–148, 2005, doi: https://doi.org/10.1002/hyp.5763.

[6] J. F. Adamowski, “Peak daily water demand forecast modeling using artificial neural networks,” J. Water Resour. Plan. Manag., vol. 134, no. 2, pp. 119–128, 2008, doi: https://doi.org/10.1061/(ASCE)0733-9496(2008)134:2(119).

[7] M. Ghiassi, D. K. Zimbra, and H. Saidane, “Urban water demand forecasting with a dynamic artificial neural network model,” J. Water Resour. Plan. Manag., vol. 134, no. 2, pp. 138–146, 2008, doi: https://doi.org/10.1061/(ASCE)0733-9496(2008)134:2(138).

[8] M. Herrera, L. Torgo, J. Izquierdo, and R. Pérez-García, “Predictive models for forecasting hourly urban water demand,” J. Hydrol., vol. 387, no. 1–2, pp. 141–150, Jun. 2010, doi: https://doi.org/10.1016/j.jhydrol.2010.04.005.

[9] B. L. Bowerman and R. T. O’Connell, “Forecasting and time series: An applied approach. 3rd,” 1993, available at : http://ecsocman.hse.ru/text/19151946/.

[10] N. Golyandina, V. Nekrutkin, and A. Zhigljavsky, Analysis of Time Series Structure, 2001, vol. 90, doi: https://doi.org/10.1201/9781420035841.

[11] J. Liao, L. Gao, and X. Wang, “Numerical Simulation and Forecasting of Water Level for Qinghai Lake Using Multi-Altimeter Data Between 2002 and 2012,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 7, no. 2, pp. 609–622, Feb. 2014, doi: https://doi.org/10.1109/JSTARS.2013.2291516.

[12] Q. Zhang, B.-D. Wang, B. He, Y. Peng, and M.-L. Ren, “Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting,” Water Resour. Manag., vol. 25, no. 11, pp. 2683–2703, Sep. 2011, doi: https://doi.org/10.1007/s11269-011-9833-y.

[13] G. Liu, D. Zhang, and T. Zhang, “Software Reliability Forecasting: Singular Spectrum Analysis and ARIMA Hybrid Model,” in 2015 International Symposium on Theoretical Aspects of Software Engineering, 2015, pp. 111–118, doi: https://doi.org/10.1109/TASE.2015.19.

[14] S. L. Zubaidi, J. Dooley, R. M. Alkhaddar, M. Abdellatif, H. Al-Bugharbee, and S. Ortega-Martorell, “A Novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks,” J. Hydrol., vol. 561, pp. 136–145, Jun. 2018, doi: https://doi.org/10.1016/j.jhydrol.2018.03.047.

[15] M. Sun, X. Li, and G. Kim, “Precipitation analysis and forecasting using singular spectrum analysis with artificial neural networks,” Cluster Comput., Jan. 2018, doi: https://doi.org/10.1007/s10586-018-1713-2.

[16] L. Latifoğlu, Ö. Kişi, and F. Latifoğlu, “Importance of hybrid models for forecasting of hydrological variable,” Neural Comput. Appl., vol. 26, no. 7, pp. 1669–1680, Oct. 2015, doi: https://doi.org/10.1007/s00521-015-1831-1.

[17] Y. Xiao, J. J. Liu, Y. Hu, Y. Wang, K. K. Lai, and S. Wang, “A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting,” J. Air Transp. Manag., vol. 39, pp. 1–11, Jul. 2014, doi: https://doi.org/10.1016/j.jairtraman.2014.03.004.

[18] M. Abdollahzade, A. Miranian, H. Hassani, and H. Iranmanesh, “A new hybrid enhanced local linear neuro-fuzzy model based on the optimized singular spectrum analysis and its application for nonlinear and chaotic time series forecasting,” Inf. Sci. (Ny)., vol. 295, pp. 107–125, Feb. 2015, doi: https://doi.org/10.1016/j.ins.2014.09.002.

[19] R. J. Hyndman and Y. Khandakar, Automatic time series for forecasting: the forecast package for R, no. 6/07. Monash University, Department of Econometrics and Business Statistics, 2007, available at: http://webdoc.sub.gwdg.de/ebook/serien/e/monash_univ/wp6-07.pdf.

[20] G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control, 3rd ed. Prentice Hall, 1994, available at: https://books.google.com/books?id=sRzvAAAAMAAJ.

[21] W. W. S. Wei, Time Series Analysis: Univariate and Multivariate Methods. Pearson Addison Wesley, 2006, available at: https://books.google.com/books?id=aY0QAQAAIAAJ.

[22] N. Golyandina and A. Zhigljavsky, Singular Spectrum Analysis for Time Series, 2013, doi: https://doi.org/10.1007/978-3-642-34913-3.

[23] H. Hassani, “Singular spectrum analysis: methodology and comparison,” J. Data Sci., vol. 5, no. 2, pp. 239–257, 2007, available at : https://mpra.ub.uni-muenchen.de/4991/.

[24] R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice. OTexts, 2018, available at: https://books.google.com/books?id=_bBhDwAAQBAJ.

[25] R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” Int. J. Forecast., vol. 22, no. 4, pp. 679–688, Oct. 2006, doi: https://doi.org/10.1016/j.ijforecast.2006.03.001.

[26] S. Makridakis and M. Hibon, “The M3-Competition: results, conclusions and implications,” Int. J. Forecast., vol. 16, no. 4, pp. 451–476, Oct. 2000, doi: https://doi.org/10.1016/S0169-2070(00)00057-1.

[27] Suhartono and M. H. Lee, “A Hybrid Approach based on Winter’s Model and Weighted Fuzzy Time Series for Forecasting Trend and Seasonal Data,” J. Math. Stat., vol. 7, no. 3, pp. 177–183, 2011, doi: https://doi.org/10.3844/jmssp.2011.177.183.

[28] Suhartono, I. Puspitasari, M. S. Akbar, and M. H. Lee, “Two-level seasonal model based on hybrid ARIMA-ANFIS for forecasting short-term electricity load in Indonesia,” in Statistics in Science, Business, and Engineering (ICSSBE), 2012 International Conference on, 2012, pp. 1–5, doi: https://doi.org/10.1109/ICSSBE.2012.6396642.

[29] H. Hassani, A. S. Soofi, and A. A. Zhigljavsky, “Predicting daily exchange rate with singular spectrum analysis,” Nonlinear Anal. Real World Appl., vol. 11, no. 3, pp. 2023–2034, Jun. 2010, doi: https://doi.org/10.1016/j.nonrwa.2009.05.008.

[30] W. Sulandari, Suhartono, Subanar, and H. Utami, “Forecasting time series with trend and seasonal patterns based on SSA,” in Science in Information Technology (ICSITech), 2017 3rd International Conference on, 2017, pp. 648–653, doi: https://doi.org/10.1109/ICSITech.2017.8257193.

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