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New developments in time series analysis can be used to determine a better spectral representation for unknown data. Any stationary process can be modeled accurately with one of the three model types: AR (autoregressive), MA (moving average) or the combined ARMA model. Generally, the best type is unknown. However, if the three models are estimated with suitable methods, a single time series model...
This paper presents MORSE (MOdel for Rainfall Statistics Estimation), a unified model for the prediction of spatial and temporal high-resolution rainfall rate statistics. Inputs to MORSE are the convective and total rain amounts cumulated in different time intervals, ranging from a few hours for the prediction of to much longer intervals...
An application of Parallel Radial Basis Function (PRBF) network model on prediction of chaotic time series is presented in this paper. The PRBF net consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of PRBF is a weighted...
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