ArticleScientific reports2025
A new automatic forecasting method based on explainable deep dendritic artificial neural network.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Forecasting practitioners need automated forecasting methods that do not involve subjective decisions. For this reason, in recent years, the literature has been trying to automate forecasting methods. Automated forecasting methods are usually based on various models or variable selection strategies and various hypothesis tests for data preprocessing. This study proposes input significance tests for model inputs of deep dendritic recurrent neural networks and model validity tests, in the context of explainability studies. The performance of the proposed tests is investigated with a simulation study. Secondly, based on the developed tests and some statistical tools, a new automatic forecasting method for deep dendritic recurrent neural networks is proposed. The forecasting ability of the proposed method is compared with other successful forecasting methods in the literature using M3 and M4 competition time series.
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