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In time series predictor evaluation, we observe that with respect to the model selection procedure there is a gap between evaluation of traditional forecasting procedures, on the one hand, and evaluation of machine learning techniques on the other hand. In traditional forecasting, it is common practice to reserve a part from the end of each time series for testing, and to use the rest of the series...
This study addresses the time series forecasting performance of sparsely connected neural networks (SCNNs). A novel type of SCNNs is presented based on the Apollonian networks. In terms of three types of publicly available benchmark data, extensive experiments were conducted to compare the forecasting performance of the proposed SCNNs, randomly connected SCNNs and traditional feed-forward neural networks...
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