Abstract
Recent developments in the identification, estimation and diagnostic checking of deseasonalized autoregressive moving-average (ARMA) models and periodic autoregressive (PAR) models are reviewed. These techniques are then used for fitting PAR and deseasonalized ARMA models to three specific monthly riverflow time series. The Akaike information criterion (AIC) and Bayes information criterion (BIC) suggest the PAR model provides the best fit. Split-sample simulation experiments show that the PAR models preserve the critical drought statistics of the historical flow sequences.
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© 1987 D. Reidel Publishing Company, Dordrecht, Holland
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Thompstone, R.M., Hipel, K.W., McLeod, A.I. (1987). Simulation of Monthly Hydrological Time Series. In: MacNeill, I.B., Umphrey, G.J., McLeod, A.I. (eds) Advances in the Statistical Sciences: Stochastic Hydrology. The University of Western Ontario Series in Philosophy of Science, vol 37. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-4792-4_4
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