Comparison of Stabilization Ability of Models for Hydrological Time Series with a Deterministic Trend

Huantian Xie, Min Xu and Dingfang Li

Mathematical Problems in Engineering, 2015, vol. 2015, 1-7

Abstract:

Under influence of climate change and human activities, deterministic trend has been detected and reported in various hydrometeorological observation records. In order to correctly model the stochastic properties, the time series has to be stabilized by removing the trend. Both detrending and differencing have been proposed to fulfill such a task. But the influence of the two stabilizing approaches on the residual series is distinguishing. In this study, ARMA models are constructed based on the above two stabilization approaches for an annual minimum daily discharge series with a deterministic trend. Comparisons are made with respect to stabilization ability, model simulation, and forecasting. Results indicate that the model based on detrending is superior to the one based on differencing in almost all the selected comparison criteria. So detrending is suggested to remove the deterministic trend before using ARMA model to fit the observed data.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:218289

DOI: 10.1155/2015/218289

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