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Detection and predictive modeling of chaos in finite hydrological time series

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Figure

Fig. 1. Monthly streamflow time series observed at the Arkansasriver.
Fig. 4. Relation between correlation exponent and embedding di-mension for Lorenz (X component), seasonal, and random series.
Fig. 5. The variation of CC and MSE with forecast lead time forwhite noise with σ=0.16.
Fig. 9.Correlation exponent vs. embedding dimension plot formixed time series consisting of Lorenz (X-component) and whitenoise.
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