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A comparative study of singular spectrum analysis, neural network, ARIMA and exponential smoothing for monthly rainfall forecasting | ||
| Journal of Mathematical Modeling | ||
| مقاله 12، دوره 11، شماره 4، اسفند 2023، صفحه 783-803 اصل مقاله (293.02 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2023.25412.2262 | ||
| نویسنده | ||
| Mohammad Kazemi* | ||
| Department of Statistics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran | ||
| چکیده | ||
| This paper investigates the accuracy of several forecasting methods for monthly rainfall forecasting. First, we study the feasibility of using the Singular Spectrum Analysis (SSA) to perform rainfall forecasts. When the time series data has the outliers, SSA might results in misleading conclusions, and thus robust methodologies should be used. Therefore, we consider the use of two robust SSA algorithms for model fit and model forecasting. The results of these forecasting approaches are compared with other commonly used time series forecasting techniques including Neural Network Autoregression (NNAR), Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS) and TBATS. The performance of these conjunction methods is compared in terms of accuracy for model fit and model forecast, using the monthly rainfall data from four rain gauge stations in Guilan province of Iran as the case study. | ||
| کلیدواژهها | ||
| Contaminated data؛ forecasting؛ singular value decomposition؛ time series | ||
| مراجع | ||
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[1] F.R. Adaryani, S. Jamshid Mousavi, F. Jafari, Short-term rainfall forecasting using machine learning-based approaches of PSO-SVR, LSTM and CNN, J. Hydrol. 614 (2022) 128463. [2] G.E. Box, G.M. Jenkins, Time Series Analysis - Forecasting and Control, Colorado State University, Holden Day, San Francisco, 1976. [3] P.J. Brockwell, R.A. Davis, Introduction to Time Series and Forecasting, Springer, New York, 1996. [4] D.S. Broomhead, G.P. King, Extracting qualitative dynamics from experimental data, Phys. D Nonlinear Phenom. 20 (1986) 217–236. [5] K.R. Gabriel, S. Zamir, Lower rank approximation of matrices by least squares with any choice of weights, Technometrics 21 (1979) 489–498. [6] N. Golyandina, A. Korobeynikov, A. Shlemov, K. Usevich, Multivariate and 2D extensions of singular spectrum analysis with the Rssa package, J. Stat. Softw. 67 (2015) 1–78. [7] N. Golyandina, V. Nekrutkin, A. Zhigljavsky, Analysis of Time Series Structure: SSA and Related Techniques, Chapman & Hall/CRC, New York, 2001. [8] N. Golyandina, A. Zhigljavsky, Singular Spectrum Analysis for Time Series, Springer, Berlin, Heidelberg, 2013. [9] N. Golyandina, A. Korobeynikov, A. Zhigljavsky, Singular Spectrum Analysis with R, Springer, Berlin, Heidelberg, 2018. [10] A. Groth, M. Ghil, Synchronization of world economic activity, Chaos: An Interdisciplinary J. Nonlinear Sci. 27 (2017) 127002. [11] H. Hassani, R. Mahmoudvand, H.N. Omer, E.S. Silva, A preliminary investigation into the effect of outlier(s) on singular spectrum analysis, Fluct. Noise Lett. 13 (2014) 1450029. [12] H. Hassani, R. Mahmoudvand, Multivariate singular spectrum analysis: A general view and new vector forecasting approach, Int. J. Energy Res. Stat. 1 (2013) 55–83. [13] D.M. Hawkins, L. Liu, S. Young, Robust singular value decomposition, Natl. Inst. Stat. Sci. 122 (2001) 1–12. [14] P.J. Huber, Robust Statistics, Wiley, New York, 1981. [15] R.J. Hyndman, G. Athanasopoulos, C. Bergmeir, G. Caceres, L. Chhay, M. O'Hara-Wild, F. Petropoulos, S. Razbash, E. Wang, F. Yasmeen, Forecast: Forecasting functions for time series and linear models, R package version 8.15, 2021. Available online: pkg.robjhyndman.com/forecast/. [16] R.J. Hyndman, A.B. Koehler, J.K. Ord, R.D. Snyder, Forecasting with Exponential Smoothing: The State Space Approach, Springer, Berlin, Heidelberg, 2008. [17] R. Hyndman, Y. Khandakar, Automatic time series forecasting: The forecast package for R, J. Stat. Softw. 27 (2008) 1–22. [18] R.J. Hyndman, G. Athanasopoulos, Forecasting: Principles and Practice, 2nd Edition, OTexts, Melbourne, 2018. [19] R.J. Hyndman, A.B. Koehler, R.D. Snyder, S. Grose, A state space framework for automatic forecasting using exponential smoothing methods, Int. J. Forecast. 18 (2002) 439–454. [20] M. Kazemi, P.C. Rodrigues, Robust singular spectrum analysis: comparison between classical and robust approaches for model fit and forecasting, Comput. Stat. (2023) 1-33. doi: 10.1007/s00180-022-01322-4. [21] M.A.R. Khan, D.S. Poskitt, A note on window length selection in singular spectrum analysis, Aust. N. Z. J. Stat. 55 (2013) 87–108. [22] N. Kumar, M. Nasser, S.C. Sarker, A new singular value decomposition based robust graphical clustering technique and its application in climatic data, J. Geol. Geogr. Geoecology 3 (2011) 227–238. [23] A.M.D. Livera, R.J. Hyndman, R.D. Snyder, Forecasting time series with complex seasonal patterns using exponential smoothing, JASA 106 (2011) 1513–1527. [24] D. Machiwal, M.K. Jha, Hydrologic Time Series Analysis: Theory and Practice, Springer, Capital Publishing Company, 2012. [25] R. Mahmoudvand, P.C. Rodrigues, A new parsimonious recurrent forecasting model in singular spectrum analysis, J. Forecast. 37 (2018) 191–200. [26] R. Mahmoudvand, N. Najari, M. Zokaei, On the optimal parameters for reconstruction and forecasting in singular spectrum analysis, Commun. Stat. Simul. Comput. 42 (2013) 860–870. [27] R. Mahmoudvand, P.C. Rodrigues, M. Yarmohammadi, Forecasting daily exchange rates: A comparison between SSA and MSSA, RevStat-Stat. J. 17 (2019) 599–616. [28] S. Makridakis, A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler, The accuracy of extrapolation (time series) methods: Results of a forecasting competition, J. Forecast. 1 (1982) 111–153. [29] S. Makridakis, M. Hibon, The M3-Competition: results, conclusions and implications, Int. J. Forecast. 16 (2000) 451–476. [30] S. Makridakis, E. Spiliotis, V. Assimakopoulos, The M4-Competition: 100,000 time series and 61 forecasting methods, Int. J. Forecast. 36 (2020) 54–74. [31] C.C. Pegels, Exponential forecasting: Some new variations, Manag. Sci. 15 (1969) 311–315. [32] M.C. Ramírez, N.J. Ferreira, H.F. Velho, Linear and nonlinear statistical downscaling for rainfall forecasting over southeastern Brazil, Weather 21 (2006) 969–989. [33] M.U.M. Rao, K.C. Patra, S.K. Sasmal, A. Sharma, G. Oliveto, Forecasting of rainfall across river basins using soft computing techniques: the case study of the upper Brahmani basin (India), Water 15 (2023) 499. [34] V.A. Reisen, F.F. Molinares, Robust estimation in time series with long and short memory properties, Ann. Math. Inform. 39 (2012) 207–224. [35] P.C. Rodrigues, A. Monteiro, V.M. Lourenço, A Robust additive main effects and multiplicative interaction model for the analysis of genotype-by-environment data, Bioinform. 32 (2016) 58–66. [36] P.C. Rodrigues, R. Mahmoudvand, Correlation analysis in contaminated data by singular spectrum analysis, Qual. Reliab. Eng. Int. 32 (2016) 2127–2137. [37] P.C. Rodrigues, J. Pimentel, P. Messala, M. Kazemi, The decomposition and forecasting of mutual investment funds using singular spectrum analysis, Entropy 22 (2020) 83. [38] P.C. Rodrigues, V.M. Lourenço, R. Mahmoudvand, A robust approach to singular spectrum analysis, Qual. Reliab. Eng. Int. 34 (2018) 1437–1447. [39] P.C. Rodrigues, R. Mahmoudvand, A new approach for the vector forecast algorithm in singular spectrum analysis, Commun. Stat. Simul. Comput. 49 (2020) 591–605. [40] M. Shahin, H.J. Van Oorschot, S.J. De Lange, Statistical Analysis in Water Resources Engineering, A.A. Balkema, Rotterdam, 1993. [41] W. Sulandari, M.H. Lee, P.C. Rodrigues, Indonesian electricity load forecasting using singular spectrum analysis, Energy 190 (2020) 116408. [42] J.W. Taylor, Exponential smoothing with a damped multiplicative trend, Int. J. Forecast. 19 (2003) 715–725. [43] A. Veloz, R. Salas, H. Allende-Cid, H. Allende, C. Moraga, Identification of lags in nonlinear autoregressive time series using a flexible fuzzy model, Neural Process. Lett. 43 (2016) 641–666. [44] R. Wang, H. Ma, G. Liu, D. Zuo, Selection of window length for singular spectrum analysis, J. Frankl. Inst. 352 (2015) 1541–1560. [45] L. Zhang, H. Shen, J.Z. Huang, Robust regularized singular value decomposition with application to mortality data, Ann. Appl. Stat. 7 (2013) 1540–1561. [46] J. Zabalza, C. Qing, P. Yuen, G. Sun, H. Zhao, J. Ren, Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging, J. Frankl. Inst. 355 (2018) 1733–1751. | ||
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