| Inthispaper,wehavepresentedanadaptiveensemblemethodforrainfallforecast.Theensembleisadaptivein
sense that the members of the ensemble are trained repeatedly.
For this purpose, we have employed strategies in repeated one-step
ahead prediction rainfall data. On the other hand, we use diverse
models and adapt the weights with which each of these models contribute
to the ensemble. We have used, a) multi-layered perceptron network
(MLPN), b) Elman recurrent neural network (ERNN), c) radial basis
function network (RBFN), and d) generalized neural network (GRNN)
as the base models in the ensemble. Each of the base models are
trained using soft splitting of the data. The proposed ensemble
method has advantages over basic ensemble methods for rainfall
forecasting in the sense that the output of this ensemble is an
adaptively weighted linear combination of the outputs of the individual
models. Moreover, during the test phase the base models are first
ranked using least angle regression (LARS). The LARS ranks the
variables (i.e., models) according to their predictive performance
(i.e., forecasting). In this way, only the higher ranked models
are kept reducing the computational complexity of the ensemble.
We have set up the case study for the proposed ensemble method
on the rainfall series of west central India. The empirical results
suggest that the integration of ranking and adaptive fitting of
the base models is advantageous than linearly combined ensemble
methods in two ways. First, the adaptive ensemble model achieves
a competent forecast performance while keeping adaptive property.
Second, it has low computational cost as the inefficient base models
are discarded while ranking the base models. |
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| adaptiveensemblemethod;leastangleregression;monthlyrainfall;one-stepaheadforecast.
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