Alsharif, M.H., Younes, M.K. & Kim, J. (2019). Time series ARIMA model for prediction of daily and monthly average global solar radiation: Tcastud Seo. Symmetry, 11: 240-257.
Aqalpour, P. & Nadi, M. (2018). Assessing the accuracy of SARIMA model in modeling and long-term forecast of average monthly temperature in different climates of Iran. Climatological Research, 9: 113-126
Asadi Zarch, M.A., Malekinezhad, H., Mobin, M.H., Dastorani, M.T. & Kousari, M.R. (2011). Drought Monitoring by Reconnaissance Drought Index (RDI) in Iran. Water Resources Management, 25: 3485-3504.
Asfaw, A., Simane, B., Hassen, A. & 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.
Batty, M. (2008). The size, scale, and shape of cities. Science, 319: 769-771.
De Gois, G., De Oliveira-Júnior, J.F., Da Silva Junior, C.A., Sobral, B.S., De Bodas Terassi, P.M. & Junior, A.H.S.L. (2020). Statistical normality and homogeneity of a 71-year rainfall dataset for the state of Rio De Janeiro Brazil. Theoretical and Applied Climatology, 141: 1573-1591.
Di Persio, L. & Frigo, M. (2016). Gibbs sampling approach to regime switching analysis of financial time series. Journal of Computational and Applied Mathematics, 300: 43-55.
Dudangeh, A., Abedi Koopai, J. & Gohari, J. (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.
Eslamian, S. (2014). Handbook of Engineering Hydrology: Engineering. Hydrology and Water Management. CRC Press, USA.
Gardfarstatisticszi, S. & Saberi, 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.
Hadizadeh, R., Eslamian, S. & Chinipardaz, R. (2013). Investigation of long-memory properties in streamflow time series in Gamasiab River, Iran, International. International Journal of Hydrology Science and Technology, 3(4): 319-350.
Helmi, M., Bakhtiari, B. & 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.
Jain, G. & Mallick, B. (2017). A study of time series models ARIMA and ETS. Available at: SSRN.
, N., Philippopoulos, K., Deligiorgi, D., Tzanis, C.G. & Karvounis, G. (2017). Multifractal scaling properties of daily air temperature time series. Chaos, Solitons and Fractals
, 98: 38-43.
Kazemzadeh, M. & Malekian, A. (2018). Homogeneity analysis of streamflow records in arid and semi-arid regions of northwestern Iran. Journal of Arid Land, 10:
Khatar, B. & Bahmani, A. (2015). Prediction of soil layer temperature using time series models. Journal of Soil Research, 29(2): 210-199.
Khazaei, M. & Mirzaei, M. (2014). Prediction of climatic variables using time series analysis of Zohreh watershed. Applied Research in Geographical Sciences, 34: 233-250.
Kocsi,T., Kovács-Székely, I. & Anda, A. (2020). Homogeneity tests and non-parametric analyses of tendencies in precipitation time series in Keszthely, Western Hungary. Theoretical and Applied Climatology, 139 :849-859.
Latif, Y., Yaoming, M., Yaseen, M., Muhammad, S. & Atif Wazir, M. (2020). Spatial analysis of temperature time series over the Upper Indus Basin (UIB) Pakistan. Theoretical and Applied Climatology, 139: 741-758.
Little, T.D. (2013). The Oxford Handbook of Quantitative Methods in Psychology, Vol. 1. Oxford University Press, UK.
Modares, R. & Eslamian, S. (2006). Modeling the time series of Zayandehrud river flow. Iranian Journal of Science and Technology, 30: 570-567.
Nury, A.H., Hasan, K. & 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.
Polwiang, S. (2020). The time series seasonal patterns of dengue fever and associated weather variables in Bangkok. BMC Infectious Diseases, 20: 1-10.
Qin, R.X., Xiao, C., Zhu, Y., Li, J., Yang, J., Gu, S., Xia, J., Su, B., Liu, Q. & Woodward, A. (2017). The interactive effects between high temperature and air pollution on mortality: A time-series analysis in Hefei, China. Science of The Total Environment, 575: 1530-1537.
Shabani, B., Mousavi Baigi, M., Jabbari Noghabi, M. & Hero, M. (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.
Soltani, S., Modarres, R. & Eslamian, S. (2007). The use of time series modeling for the determination of rainfall climates of Iran. International Journal of Climatology, 27: 819-829.
Zhang, J., Zhao, Z., Xue, Y., Chen, Z., Ma, X. & Zhou, Q. (2017). Time series analysis. Handbook of Medical Statistics. Journal of Physics and Chemistry of Solids, 4: 269-285.
Zhongda, T., Shujiang, L., Yanhong, W. & Yi, S.J.C. (2017). A prediction method based on wavelet transform and multiple models’ fusion for chaotic time series. Chaos, Solitons and Fractals, 98: 158-172.
Zhou, Z., Wang, L., Lin, A., Zhang, M. & Niu, Z. (2018). Innovative trend analysis of solar radiation in China during. Renewable Energy, 119: 1962-2015.