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This work presents an award winning approach for solving the NN3 forecasting competition problem. It consisted of predicting 18 future values of 111 monthly short time series. This approach consists of applying the median value of a 15-MLP ensemble for predicting each time series. The system performed very well on test data, finishing as the second best solution of the competition with a SMAPE=16...
The problem of finding the most appropriate subset of features or regressors is the generic challenge of Machine Learning problems like regression estimation or pattern recognition. We consider the problem of time-varying regression estimation, which implies also the inevitable necessity to choose the individual appropriate levels of model volatility in each of regressors, ranging from the full stationarity...
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