Times series data analysis: The Holt-Winters model for rainfall prediction In West Java

Eko Primadi Hendri , Sarah Fadhlia

Abstract


Time series data analysis is used to analyze data that considers time and data characteristics to predict future events. One of the time series data is rainfall data. Rainfall data has a seasonal pattern because there is a pattern that repeats itself over a certain period. Data analysis that considers the characteristics of seasonal patterns is the Holt-Winters method. The Holt-Winters model is divided into two, namely additive and multiplicative models. This research aims to compare the Holt-Winters additive and multiplicative methods to see the accuracy in predicting rainfall data in West Java. The additive model has level parameter α=0,435, trend parameter β=0, seasonal parameter γ=1, and RMSE value 140,174. The multiplicative model has level parameter α=0,936, trend parameter β=0, seasonal parameter γ=0,247, and RMSE value 150,020. The additive model has a smaller RMSE value so it can predict future rainfall with greater accuracy.

 


Keywords


Holt-Winters; Rainfall; Seasonal; Times Series

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References


Aini, A.N., Intan, P.K., & Ulinnuha, N. (2021). Prediction of average of monthly rain in pasuruan using the Holt-Winters exponential smoothing method. Jurnal Riset Sains dan Teknologi. 5(2), 117-122. https://doi.org/10.30595/jrst.v5i2.9702

Aini, N.N., Iriany, A., Nugroho, W.H., & Wibowo, F.L. (2022). Comparison of adaptive Holt-Winters exponential smoothing and recurrent neural network model for forecasting rainfall in Malang City. CompTech: Computer, Mathematics, and Engineering Application. 13(2), 87-96. https://doi.org/10.21512/comtech.v13i2.7570

Ardian, E., Karmini, M., & Budiman. (2011). Adaptasi dan mitigasi perubahan iklim di Indonesia. Pusat perubahan iklim dan kualitas udara kedeputian bidang klimatologi. Jakarta: BMKG.

Chai, T., & Draxler, R.R., (2014). Root Mean Square Error (RMSE) or Mean Absolute Error (MAE) – arguments against avoiding RMSE in the literature. Geosci Model Dev. 7, 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014

Fan, J., Lui, X., Li, Z., Wang, X., Cao, S., & Lei, J. (2021). Power load forecasting research based on neural network and Holt-Winters method. IOP Conf. Series: Earth and Environmental Science. https://doi:10.1088/1755-1315/692/2/022120

Firdaus. (2006). Analisis deret waktu satu ragam. Bogor: IPB Press.

Hanke, J.E. and Wichern, D.W. (2005). Business forecasting eight edition. New Jersey: Pearson Prentice Hall.

Hamidah, Nusyirwan, & Faisol, Ahmad. (2020). Forecasting seasonal time series data using the holt-winters exponential smoothing methods of additive models. Jurnal Matematika Integratif, 16(2), 151-157. https://doi.org/10.24198/jmi.v16.n2.29293.151-157

Holt, C.C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting. 20(1), 5-10.

https://doi.org/10.1016/j.ijforecast.2003.09.015

Hutapea, T.A., & Siahaan, A.Y. (2023). Rainfall forecasting using the holt-winters exponential smoothing method in North Padang Lawas. Journal of Student Research, 1(2), 378-393. https://doi.org/10.55606/jsr.v1i2.1046

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts.

Irwan, Abdy, M., Karwangsic, E., & Ahmar, A.S. (2023). Rainfall forecasting in Makassar city using triple exponential smoothing method. ARRUS Journal of Social Sciences and Humanities, 3(1), 52-58. https://doi.org/10.35877/soshum1707

Kalekar, P.S. (2004). Time series forecasting using Holt-Winters exponential smoothing. India: Kanwal Rekhi School of Information Technology.

Kamruzzaman. M, Beecham, S., & Metcalfe, A.V. (2011). Non-stationarity in rainfall and temperature in Murray Darling Basin. Hydrological Processes, 25(10), 1659-1675.

https://doi.org/10.1002/hyp.7928

Lui, L., & Wu, L. (2022). Holt-Winters model with grey generation operation and its application. Communication in Statistics – Theory and Methods, 51(11), 3542-3555. https://doi.org/10.1080/036109 26.2020.1797804

Makridakis, S., Wheelwright, S.C., & Mc Gee, V.E., (1999). Methods and applications of forecasting. 2nd edition ed. Jakarta: Erlangga.

Pertiwi, D.D., (2020). Applied exponential smoothing Holt-Winters method for rainfall forecast in Mataram city. Journal of Intelligent Computing and Health Informatics, 1(2), 46-49. https://doi.org/10.26714/jichi.v1i2.6330

Rafian, M. Dzaky. (2022). Rainfall data analysis using the Spearman methods, Mann-Kendall methods, and Holt-Winters exponential smoothing. Bogor: Institut Pertanian Bogor.

Rosalina, Encik. (2015). Holt-Winter forecasting method to predict the number of visitors to the Riau University library. Riau: Universitas Riau.

Sinay, L.J., Pentury, Th., and Anakotta, D. (2017). Rainfall forecasting in Ambon city using the Holt-Winters exponential smoothing method. Barekeng: Jurnal Ilmu matematika dan Terapan, 11(2), 101-108. https://doi.org/10.30598/barekengvol11iss2pp101-108

Wesli, Ir., (2008). Drainase Perkotaan. Yogyakarta: Graha ilmu.

Wiguna, I.K.A.G., Utami, N.L.P.A.C., Parwita, W.G.S., Udayama, I.P.A.E.D., & Sudipa, I.G.I (2023). Rainfall forecasting using the Holt-Winters exponential smoothing method. Jurnal Info Sains: Informatikan dan Sains, 13(1), 15-23.

Winters, P.R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324-342. https://doi.org/10.1287/mnsc.6.3.324

Yang, Y., Yu, H., & Sun, Z. (2017). Aircraft failure rate forecasting method based on Holt-Winters seasonal model. IEEE 2nd International Conference on Cloud Computing and Big Data Analysis, 520-524. https://doi.org/10.1109/ICCCBDA.2017.7951969

Zhu, G., Li, L., Zheng, Y., Zhang, X., & Zou, H. (2021) Forecasting influenza based on autoregressive moving average and Holt-Winters exponential smoothing models. Journal of advanced Computation Intelligence and Intelligent Informatics, 25(1), 138-144.

https://doi.org/10.20965/jaciii.2021.p0138




DOI: https://doi.org/10.58524/app.sci.def.v2i1.325

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