1.Alencar D.B., Affonso C.M., Oliveira R.C., and Jose Filho C.R. 2018. Hybrid approach combining SARIMA and neural networks for multi-step ahead wind speed forecasting in Brazil. IEEE Access, 6: 55986-55994.
https://doi.org/ 1109/ACCESS.2018.2872720
2.Alsharif M.H., Younes M.K. and Kim J. 2019. Time series ARIMA model for prediction of daily and monthly average global solar radiation: Tcastud Seo. Symmetry, 11: 240-257.
https://doi.org/10.3390/sym11020240
3.Asfaw A., Simane B., Hassen A. and Bantider A. 2018. Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: A case study in Woleka sub-basin. Weather and Climate Extremes, 19:29-41.
https://doi.org/10.1016/j.wace.2017.12.002
4.Chen J., Zeng G.Q., Zhou W., Du W., and Lu K.D. 2018. Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization. Energy conversion and management, 165, 681-695.
https://doi.org/10.1016/j.enconman.2018.03.098
5.Dudangeh A., Abedi Koopai J., and Gohari A. 2012. Application of time series models to determine the trend of future climatic parameters in order to manage water resources. Water and Soil Science, 16: 59-74 (In Persian).
6.Eslamian S. 2014. Handbook of Engineering Hydrology Engineering Hydrology and Water Management. CRC Press, USA.
7.Gardfaramarzi S., Saberi, and Qaisouri A. 2017. Determining the best time series model in forecasting annual rainfall of selected stations in West Azerbaijan province. Applied Research in Geographical Sciences, 17: 87-105 (In Persian).
8.Helmi M., Bakhtiari B. and Ghaderi K. 2020. Modeling and forecasting of meteorological drought using SARIMA time series model in different climatic samples of Iran, Iranian Journal of Irrigation and Drainage, 14:1090-1079 (In Persian).
9.Kalamaras N., Philippopoulos K., Deligiorgi D., Tzanis C.G. and Karvounis G. 2017. Multifractal scaling properties of daily air temperature time series. Chaos, Solitons & Fractals, 98: 38-43.
https://doi.org/10.1016/j.chaos.2017.03.003
10.Khazaei M., and Mirzaei M. 2014. Prediction of climatic variables using time series analysis of Zohreh watershed. Applied Research in Geographical Sciences, 34: 233-250 (In Persian).
11.Khtar B., and Bahmani A. 2015. Prediction of soil layer temperature using time series models. Journal of Soil Research (Soil and Water Sciences), 210-199 (In Persian).
13.Little T.D. 2013. The Oxford Handbook of Quantitative Methods in Psychology, Oxford University Press.
14.Modares R., and Eslamian S. Modeling the time series of Zayandehrud river flow. Iranian Journal of Science and Technology, 30:570-567 (In Persian).
15.Nazaripour H., Karimi Z., and Sedaghat M. 2012. Hydrometeorological drought assessment based on the integrated drought index and its prediction with Markov chain in Sarbaz river basin (southeast of Iran). Journal of Soil and Water Sciences, 20: 169-151 (In Persian).
16.Nury A.H., Hasan, K. and Alam, M.J.B. 2017. Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh. Journal of King Saud University Science
, 29:47-61.
https://doi.org/10.1016/j.jksus.2015.12.002
17.Shabani B., Mousavi Baigi M., Jabbari Noghabi M., and Hero B. 2013. Modeling and forecasting the maximum and minimum monthly temperatures of Mashhad plain using time series models. Journal of Water and Soil, 27: 906-896 (In Persian).
18.Qahraman N., and Qarakhani, A. 2012. Evaluation of time series stochastic models in estimating evaporation from the pan (Case study: Shiraz station). Water Research in Agriculture (Soil and Water Sciences), 25: 81-75 (In Persian).
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