A Comparison of Nonlinear Stochastic Self-Exciting Threshold Autoregressive and Chaotic k-Nearest Neighbour Models in Daily Streamflow Forecasting

Hakan Tongal () and Martijn Booij ()

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2016, vol. 30, issue 4, 1515-1531

Abstract: A nonlinear stochastic self-exciting threshold autoregressive (SETAR) model and a chaotic k-nearest neighbour (k-nn) model, for the first time, were compared in one and multi-step ahead daily flow forecasting for nine rivers with low, medium, and high flows in the western United States. The embedding dimension and the number of nearest neighbours of the k-nn model and the parameters of the SETAR model were identified by a trial-and-error process and a least mean square error estimation method, respectively. Employing the recursive forecasting strategy for the first time in multi-step forecasting of SETAR and k-nn, the results indicated that SETAR is superior to k-nn by means of performance indices. SETAR models were found to be more efficient in forecasting flows in one and multi-step forecasting. SETAR is less sensitive to the propagated error variances than the k-nn model, particularly for larger lead times (i.e., 5 days). The k-nn model should carefully be used in multi-step ahead forecasting where peak flow forecasting is important by considering the risk of error propagation. Copyright Springer Science+Business Media Dordrecht 2016

Keywords: Multi-step flow forecasting; Nonlinear stochastic self-exciting threshold autoregressive (SETAR) model; k-nearest neighbour (k-nn) model; Western United States (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11269-016-1237-6

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