A New Automatic Forecasting Method Based on Explainable Deep Dendritic Artificial Neural Network

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 model or variable selection strategies and various hypothesis tests for data preprocessing. This study proposes input significancy 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.