Keywords and phrases: seasonal ARIMA, Holt-Winter additive, time series analysis, Rainfall forecasting, combined forecasts.
Received: August 1, 2023; Accepted: October 21, 2023; Published: November 9, 2023
How to cite this article: D. Karthika and K. Karthikeyan, Performance of combined forecasting model for monthly rainfall precipitation, Advances and Applications in Statistics 90(1) (2023), 111-130. http://dx.doi.org/10.17654/0972361723066
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
References:
[1] J. Armstrong, Combining forecasts, Principles of Forecasting: A Handbook for Researchers and Practitioners, J. S. Armstrong, ed., Kluwer Academic, Boston, 2001. [2] G. R. Alonso Brito et al., Comparison between SARIMA and Holt-Winters models for forecasting monthly stream flow in the western region of Cuba, SN Appl. Sci. 3 (2021), 671. https://doi.org/10.1007/s42452-021-04667-5. [3] J. M. Bates and C. W. Granger, The combination of forecasts, Journal of the Operational Research Society 20(4) (1969), 451-468. https://doi.org/10.1057/jors.1969.103. [4] G. E. P. Box, G. M. Jenkins and G. C. Reinsel, Time Series Analysis; Forecasting and Control, 3rd ed., Prentice Hall, Englewood Cliff, New Jersey, 1994. [5] J. Caiado, Performance of combined double seasonal univariate time series models for forecasting water demand, Journal of Hydrologic Engineering 15(3) (2010), 215-222. https://doi.org/10.1061/(asce)he.1943-5584.0000182. [6] S. Chattopadhyay, Feed forward artificial neural network model to predict the average summer-monsoon rainfall in India, Acta Geophys. 55 (2007), 369-382. https://doi.org/10.2478/s11600-007-0020-8. [7] R. Clemen, Combining forecasts: a review and an noted bibliography, Int. J. Forecast. 5 (1989), 559-583. [8] M. Dastorani, M. Mirzavand, M. T. Dastorani and S. J. Sadatinejad, Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition, Natural Hazards 81(3) (2016), 1811-1827. [9] Deepak Kumar, Anshuman Singh, Pijush Samui and Rishi Kumar Jha, Forecasting monthly precipitation using sequential modelling, Hydrological Sciences Journal 64(6) (2019), 690-700. DOI: 10.1080/02626667.2019.1595624. [10] D. Eni and F. J. Adeyeye, Seasonal ARIMA modeling and forecasting of rainfall in Warri Town, Nigeria, Journal of Geoscience and Environment Protection 3(6) (2015), 91. 10.4236/gep.2015.36015. [11] Z. Hajirahimi and M. Khashei, Hybrid structures in time series modeling and forecasting: A Review. Eng. Appl. Artif. Intell. 86 (2019), 83-106. [12] C. C. Holt, Forecasting seasonals and trends by exponentially weighted moving averages, International Journal of Forecasting 20(1) (2004), 5-10. [13] D. Karthika and K. Karthikeyan, Estimation of electrical energy consumption in Tamil Nadu using univariate time-series analysis, Annals of Optimization Theory and Practice 4(2) (2021), 31-37. https://doi.org/10.22121/aotp.2021.292718.1073. [14] D. Karthika and K. Karthikeyan, Analysis of mathematical models for rainfall prediction using seasonal rainfall data: a case study for Tamil Nadu, India, 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) 2022, pp. 01-04. https://doi.org/10.1109/iceeict53079.2022.9768602. [15] A. Lama, K. N. Singh, H. Singh, R. Shekhawat, P. Mishra and B. Gurung, Forecasting monthly rainfall of Sub-Himalayan region of India using parametric and non-parametric modelling approaches, Model. Earth Syst. Environ. 8 (2022), 837-845. https://doi.org/10.1007/s40808-021-01124-5. [16] S. Mehdizadeh, F. Fathian, M. J. S. Safari and J. F. Adamowski, A comparative assessment of time series and artificial intelligence models for estimating monthly stream flow: local and external data analyses approach, Journal of Hydrology (2019), Article No. 124225. https://doi.org/10.1016/j.jhydrol.2019.124225. [17] M. Mirzavand, S. J. Sadatinejad, H. Ghasemieh, R. Imani and M. S. Motlagh, Prediction of ground water level in arid environment using a non-deterministic model, Journal of Water Resource and Protection 6(7) (2014), 669-676. https://doi.org/10.4236/jwarp.2014.67064. [18] M. Mirzavand and R. Ghazavi, A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods, Water Resources Management 29(4) (2015), 1315-1328. https://doi.org/10.1007/s11269-014-0875-9. [19] Mohammad Mehdi Moghimi, Abdol Rassoul Zarei and Mohammad Reza Mahmoudi, Seasonal drought forecasting in arid regions, using different time series models and RDI index, Journal of Water and Climate Change 11(3) (2020), 633-654. doi: https://doi.org/10.2166/wcc.2019.009. [20] K. V. Narasimha Murthy, R. Saravana and K. Vijaya Kumar, Modeling and forecasting rainfall patterns of southwest monsoons in North-East India as a SARIMA process, Meteorol. Atmos. Phys. 130 (2018), 99-106. DOI: https://doi.org/10.1007/s00703-017-0504-2. [21] J. E. Nash and J. V. Sutcliffe, River flow forecasting through conceptual models, Part I-a discussion of principles, J. Hydrol. 27(3) (1970), 282-290. [22] A. Najafabadipour, G. Kamali and H. Nezamabadi-pour, The innovative combination of time series analysis methods for the forecasting of groundwater fluctuations, Water Resources 49(2) (2022), 283-291. https://doi.org/10.1134/s0097807822020026. [23] P. Newbold and C. W. Granger, Experience with forecasting univariate time series and the combination of forecasts, Journal of the Royal Statistical Society: Series A (General) 137(2) (1974), 131-146. https://doi.org/10.2307/2344546. [24] M. Nyatuame and S. K. Agodzo, Stochastic ARIMA model for annual rainfall and maximum temperature forecasting over Tordzie watershed in Ghana, Journal of Water and Land Development 37 (2018), 127-140. DOI: 10.2478/jwld-2018-0032. [25] G. Papacharalampous, H. Tyralis and D. Koutsoyiannis, Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes, Stochastic Environmental Research and Risk Assessment 33(2) (2019), 481-514. https://doi.org/10.1007/s00477-018-1638-6. [26] Y. J. Puah, Y. F. Huang, K. C. Chua and T. S. Lee, River catchment rainfall series analysis using additive Holt-Winters method, Journal of Earth System Science 125(2) (2016), 269-283. https://doi.org/10.1007/s12040-016-0661-6. [27] S. Ray, S. S. Das, P. Mishra and A. M. Ghazi Al Khatib, Time series SARIMA modelling and forecasting of monthly rainfall and temperature in the South Asian countries, Earth Syst. Environ. 5 (2021), 531-546. https://doi.org/10.1007/s41748-021-00205-w. [28] S. Soltani, R. Modarres and S. S. Eslamian, The use of time series modeling for the determination of rainfall climates of Iran, Int. J. Climatol. 27 (2007), 819-829. Doi: https://doi.org/10.1002/joc.1427. [29] M. Valipour, Long‐term runoff study using SARIMA and ARIMA models in the United States, Meteorological Applications 22(3) (2015), 592-598. https://doi.org/10.1002/met.1491. [30] P. R. Winters, Forecasting sales by exponentially weighted moving averages, Manag. Sci. 6(3) (1960), 324-342. https://doi.org/10.1287/mnsc.6.3.324. [31] R. L. Winkler and S. Makridakis, The combination of forecasts, Journal of the Royal Statistical Society: Series A (General) 146(2) (1983), 150-157. https://doi.org/10.2307/2982011.
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