In this paper, a novel WASD (weights and structure determination) algorithm is presented for the poweractivation feed-forward neuronet (PFN) to solve monthly time series modeling and forecasting problems. Besides, a simple and effective data preprocessing approach is employed. Based on the WDD (weights direct determination) method and the relationship between the structure and the performance of PFN, the WASD algorithm can determine the weights and the optimal structure (i.e., the optimal numbers of input-layer and hidden-layer neurons) of the PFN. Numerical experiment results further substantiate the superiority of the PFN equipped with the WASD algorithm to model and forecast monthly time series from M forecasting competition.
In this paper, a novel weighted-combination-ofcomponents (WCC) method is proposed for modeling and forecasting trend and seasonal time series, and such a method is based on decomposition model which regards the time series as the weighted combination of trend, seasonality and other components. Specifically, the Holt's two-parameter exponential smoothing (HTPES) method is improved (for short, the IHTPES method) to evaluate the trend with linearly declining increments; and the multiple sine functions decomposition (MSFD) method is developed to evaluate the seasonality. Then the weighted combination of the evaluations is obtained to estimate the global time series. Numerical experiment results substantiate the effectiveness and superiority of the proposed WCC method in terms of modeling and forecasting time series from the NN3 competition.
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